subject:(support vector machine)

1. Sangeetha, R. A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;.

Degree: Computer Science, 2015, Avinashilingam Deemed University For Women

URL: http://shodhganga.inflibnet.ac.in/handle/10603/36872

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With the massive amount of data being generated in everyday life it is increasingly important to develop a powerful framework for analysis interpretation and extraction… (more)

Subjects/Keywords: Support vector machine; kernel function; Hilbert space

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APA (6^th Edition):

Sangeetha, R. (2015). A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;. (Thesis). Avinashilingam Deemed University For Women. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/36872

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sangeetha, R. “A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;.” 2015. Thesis, Avinashilingam Deemed University For Women. Accessed March 06, 2021. http://shodhganga.inflibnet.ac.in/handle/10603/36872.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sangeetha, R. “A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;.” 2015. Web. 06 Mar 2021.

Vancouver:

Sangeetha R. A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;. [Internet] [Thesis]. Avinashilingam Deemed University For Women; 2015. [cited 2021 Mar 06]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/36872.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sangeetha R. A Framework for Admissible Kernel Function in Support Vector Machines using Lévy Distribution;. [Thesis]. Avinashilingam Deemed University For Women; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/36872

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

2. Tan, Kun. Vibration Data Analysis for Worker-Level Productivity Tracking in Construction Projects.

Degree: MS, Construction Management, 2018, Texas A&M University

URL: http://hdl.handle.net/1969.1/174154

► The construction industry has been suffering from declining productivity since the 1950s. To tackle this issue, our industry has developed numerous productivity-tracking methods and systems.… (more)

Subjects/Keywords: Support Vector Machine; Construction Action; Productivity Tracking

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APA (6^th Edition):

Tan, K. (2018). Vibration Data Analysis for Worker-Level Productivity Tracking in Construction Projects. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/174154

Chicago Manual of Style (16^th Edition):

Tan, Kun. “Vibration Data Analysis for Worker-Level Productivity Tracking in Construction Projects.” 2018. Masters Thesis, Texas A&M University. Accessed March 06, 2021. http://hdl.handle.net/1969.1/174154.

MLA Handbook (7^th Edition):

Tan, Kun. “Vibration Data Analysis for Worker-Level Productivity Tracking in Construction Projects.” 2018. Web. 06 Mar 2021.

Vancouver:

Tan K. Vibration Data Analysis for Worker-Level Productivity Tracking in Construction Projects. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/1969.1/174154.

Council of Science Editors:

Tan K. Vibration Data Analysis for Worker-Level Productivity Tracking in Construction Projects. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/174154

University of Nairobi

3. Wagacha, Peter W. Support vector machines: A Critical empirical evaluation .

Degree: 2003, University of Nairobi

URL: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/24232

► The main focus of this thesis is Support Vector Xlachines (8\-:'1s). In particular, we first investigate empirically whether they live up to the claims made,… (more)

Subjects/Keywords: Support Vector Machine

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APA (6^th Edition):

Wagacha, P. W. (2003). Support vector machines: A Critical empirical evaluation . (Thesis). University of Nairobi. Retrieved from http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/24232

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wagacha, Peter W. “Support vector machines: A Critical empirical evaluation .” 2003. Thesis, University of Nairobi. Accessed March 06, 2021. http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/24232.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wagacha, Peter W. “Support vector machines: A Critical empirical evaluation .” 2003. Web. 06 Mar 2021.

Vancouver:

Wagacha PW. Support vector machines: A Critical empirical evaluation . [Internet] [Thesis]. University of Nairobi; 2003. [cited 2021 Mar 06]. Available from: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/24232.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wagacha PW. Support vector machines: A Critical empirical evaluation . [Thesis]. University of Nairobi; 2003. Available from: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/24232

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

4. Liscio, Enrico (author). Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50

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Autonomous grasping is a key requisite for the autonomy of robots. However, grasping of unknown objects in domestic environments is difficult due to the presence… (more)

Subjects/Keywords: Machine Learning; Robotics; Grasping; Support Vector Machines

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APA (6^th Edition):

Liscio, E. (. (2017). Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50

Chicago Manual of Style (16^th Edition):

Liscio, Enrico (author). “Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach.” 2017. Masters Thesis, Delft University of Technology. Accessed March 06, 2021. http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50.

MLA Handbook (7^th Edition):

Liscio, Enrico (author). “Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach.” 2017. Web. 06 Mar 2021.

Vancouver:

Liscio E(. Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Mar 06]. Available from: http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50.

Council of Science Editors:

Liscio E(. Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:261fb6d5-bcda-471a-907a-e3fdcec20e50

5. 中田, 貴之. 対象の値動きの方向性に着目した経済時系列予測へのサポートベクターマシンの応用に関する研究 : Research regarding the Application of Support Vector Machines to Predict the Direction of Price Changes in Economic Time Series.

Degree: 修士(情報理工学), 2017, The University of Tokyo / 東京大学

URL: http://hdl.handle.net/2261/28804

► Today, time series data are predicted using various methods. The main technique currently used to identify a time series has been adjusting the coefficient parameter… (more)

Subjects/Keywords: Support Vector Machine

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APA (6^th Edition):

中田, �. (2017). 対象の値動きの方向性に着目した経済時系列予測へのサポートベクターマシンの応用に関する研究 : Research regarding the Application of Support Vector Machines to Predict the Direction of Price Changes in Economic Time Series. (Thesis). The University of Tokyo / 東京大学. Retrieved from http://hdl.handle.net/2261/28804

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

中田, 貴之. “対象の値動きの方向性に着目した経済時系列予測へのサポートベクターマシンの応用に関する研究 : Research regarding the Application of Support Vector Machines to Predict the Direction of Price Changes in Economic Time Series.” 2017. Thesis, The University of Tokyo / 東京大学. Accessed March 06, 2021. http://hdl.handle.net/2261/28804.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

中田, 貴之. “対象の値動きの方向性に着目した経済時系列予測へのサポートベクターマシンの応用に関する研究 : Research regarding the Application of Support Vector Machines to Predict the Direction of Price Changes in Economic Time Series.” 2017. Web. 06 Mar 2021.

Vancouver:

中田 �. 対象の値動きの方向性に着目した経済時系列予測へのサポートベクターマシンの応用に関する研究 : Research regarding the Application of Support Vector Machines to Predict the Direction of Price Changes in Economic Time Series. [Internet] [Thesis]. The University of Tokyo / 東京大学; 2017. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/2261/28804.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

中田 �. 対象の値動きの方向性に着目した経済時系列予測へのサポートベクターマシンの応用に関する研究 : Research regarding the Application of Support Vector Machines to Predict the Direction of Price Changes in Economic Time Series. [Thesis]. The University of Tokyo / 東京大学; 2017. Available from: http://hdl.handle.net/2261/28804

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University College Cork

6. Belur Nagaraj, Sunil. Neonatal EEG classification using atomic decomposition.

Degree: 2015, University College Cork

URL: http://hdl.handle.net/10468/2117

► The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant.… (more)

▼ The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings. Advisors/Committee Members: Lightbody, Gordon, Marnane, William P., Boylan, Geraldine B., Stevenson, Nathan J., SFI.

Subjects/Keywords: Neonatal electroencephalogram; Atomic decomposition; Support vector machine

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APA (6^th Edition):

Belur Nagaraj, S. (2015). Neonatal EEG classification using atomic decomposition. (Thesis). University College Cork. Retrieved from http://hdl.handle.net/10468/2117

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Belur Nagaraj, Sunil. “Neonatal EEG classification using atomic decomposition.” 2015. Thesis, University College Cork. Accessed March 06, 2021. http://hdl.handle.net/10468/2117.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Belur Nagaraj, Sunil. “Neonatal EEG classification using atomic decomposition.” 2015. Web. 06 Mar 2021.

Vancouver:

Belur Nagaraj S. Neonatal EEG classification using atomic decomposition. [Internet] [Thesis]. University College Cork; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10468/2117.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Belur Nagaraj S. Neonatal EEG classification using atomic decomposition. [Thesis]. University College Cork; 2015. Available from: http://hdl.handle.net/10468/2117

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rice University

7. Wilhelm, Keith. Classification Techniques for Undersampled Electromyography and Electrocardiography.

Degree: MS, Engineering, 2012, Rice University

URL: http://hdl.handle.net/1911/77325

► Electrophysiological signals including electrocardiography (ECG) and electromyography (EMG) are widely used in clinical environments for monitoring of patients and for diagnosis of conditions including cardiac… (more)

Subjects/Keywords: Electromyography; Electrocardiography; Support vector machine; Compressive sensing

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APA (6^th Edition):

Wilhelm, K. (2012). Classification Techniques for Undersampled Electromyography and Electrocardiography. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/77325

Chicago Manual of Style (16^th Edition):

Wilhelm, Keith. “Classification Techniques for Undersampled Electromyography and Electrocardiography.” 2012. Masters Thesis, Rice University. Accessed March 06, 2021. http://hdl.handle.net/1911/77325.

MLA Handbook (7^th Edition):

Wilhelm, Keith. “Classification Techniques for Undersampled Electromyography and Electrocardiography.” 2012. Web. 06 Mar 2021.

Vancouver:

Wilhelm K. Classification Techniques for Undersampled Electromyography and Electrocardiography. [Internet] [Masters thesis]. Rice University; 2012. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/1911/77325.

Council of Science Editors:

Wilhelm K. Classification Techniques for Undersampled Electromyography and Electrocardiography. [Masters Thesis]. Rice University; 2012. Available from: http://hdl.handle.net/1911/77325

University of Technology, Sydney

8. Nuryani, N. Electrocardiogram and hybrid support vector algorithms for detection of hypoglycaemia in patients with type 1 diabetes.

Degree: 2013, University of Technology, Sydney

URL: http://hdl.handle.net/10453/21902

► Hypoglycaemia is the most acute and common complication of type 1 diabetes. Physiological changes occur when blood glucose concentration falls to a certain level. A… (more)

▼ Hypoglycaemia is the most acute and common complication of type 1 diabetes. Physiological changes occur when blood glucose concentration falls to a certain level. A number of studies have demonstrated that hypoglycaemia causes electrocardiographic (ECG) alteration. The serious harmful effects of hypoglycaemia on the body motivate research groups to find an optimal strategy to detect it. Detection of hypoglycaemia can be performed by puncturing the skin to measure the blood glucose level. However, this method is unsuitable as frequent puncturing may produce anxiety in patients and periodic puncturing is difficult to conduct, not to mention inconvenient, while the patient is sleeping. Therefore, a continuous and non-invasive technique can be considered for hypoglycaemia detection. Several techniques have been reported, such as reverse iontophoresis and absorption spectroscopy. Another approach to hypoglycaemia detection is based on the physiological effects of hypoglycaemia on the various parts of the body such as the brain, heart and skin. Physiological effects of hypoglycemia to the brain are studied by investigating electroencephalography (EEG) features. Hypoglycemic effects to the heart include alteration of electrocardiographic (ECG) parameters such as heart rate, QT intervals and T-wave amplitude alteration. Several algorithms were developed to process ECG parameters for hypoglycemia detection. The algorithms include neural network and fuzzy system based intelligent algorithms. Furthermore, hybrid systems were also developed, such as fuzzy neural network and genetic-algorithm-based multiple regression with fuzzy inference systems. So far, hypoglycaemia detection systems which are based on the physiological effects still require extensive validation before they can be adopted for worldwide clinical practices. The research in this thesis introduces several ECG parameters especially which relate to the repolarization phase and could contribute to hypoglycaemia detection. Furthermore, this research aims to introduce novel computational intelligent techniques for hypoglycaemia detection. The detection is based on electrocardiographic (ECG) parameters. A support vector machine (SVM) is the first algorithm introduced for hypoglycaemia detection in this research. The second algorithm is a hybrid of SVM with particle swarm optimization (PSO), which is called an SSVM algorithm. This algorithm is intended to improve the performance of the first algorithm. PSO is an evolutionary technique based on the movement of swarms. It is employed to optimize SVM parameters in order that the SVM perform well for hypoglycaemia detection. The third algorithm is for the improvement of the second algorithm where a fuzzy inference system (FIS) is included. This algorithm involves SVM, FIS and a PSO, which is called SFSVM. The FIS is used to process some ECG parameters to find a better performance of hypoglycaemia detection. FIS is an effective intelligent system which employs fuzzy logic and fuzzy set theory. Its frameworks are based on…

Subjects/Keywords: Diabetes.; Hypoglycemia.; Electrocardiogram.; Support vector machine.; Diagnosis.

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APA (6^th Edition):

Nuryani, N. (2013). Electrocardiogram and hybrid support vector algorithms for detection of hypoglycaemia in patients with type 1 diabetes. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/21902

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nuryani, N. “Electrocardiogram and hybrid support vector algorithms for detection of hypoglycaemia in patients with type 1 diabetes.” 2013. Thesis, University of Technology, Sydney. Accessed March 06, 2021. http://hdl.handle.net/10453/21902.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nuryani, N. “Electrocardiogram and hybrid support vector algorithms for detection of hypoglycaemia in patients with type 1 diabetes.” 2013. Web. 06 Mar 2021.

Vancouver:

Nuryani N. Electrocardiogram and hybrid support vector algorithms for detection of hypoglycaemia in patients with type 1 diabetes. [Internet] [Thesis]. University of Technology, Sydney; 2013. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10453/21902.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nuryani N. Electrocardiogram and hybrid support vector algorithms for detection of hypoglycaemia in patients with type 1 diabetes. [Thesis]. University of Technology, Sydney; 2013. Available from: http://hdl.handle.net/10453/21902

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of New Mexico

9. Anderson, Blake. Integrating Multiple Data Views for Improved Malware Analysis.

Degree: Department of Computer Science, 2014, University of New Mexico

URL: http://hdl.handle.net/1928/24289

► Malicious software (malware) has become a prominent fixture in computing. There have been many methods developed over the years to combat the spread of malware,… (more)

Subjects/Keywords: Malware; Support Vector Machine; Classification; Clustering; Phylogeny

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APA (6^th Edition):

Anderson, B. (2014). Integrating Multiple Data Views for Improved Malware Analysis. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/24289

Chicago Manual of Style (16^th Edition):

Anderson, Blake. “Integrating Multiple Data Views for Improved Malware Analysis.” 2014. Doctoral Dissertation, University of New Mexico. Accessed March 06, 2021. http://hdl.handle.net/1928/24289.

MLA Handbook (7^th Edition):

Anderson, Blake. “Integrating Multiple Data Views for Improved Malware Analysis.” 2014. Web. 06 Mar 2021.

Vancouver:

Anderson B. Integrating Multiple Data Views for Improved Malware Analysis. [Internet] [Doctoral dissertation]. University of New Mexico; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/1928/24289.

Council of Science Editors:

Anderson B. Integrating Multiple Data Views for Improved Malware Analysis. [Doctoral Dissertation]. University of New Mexico; 2014. Available from: http://hdl.handle.net/1928/24289

University of Georgia

10. Fatehpuria, Naman. Parallel Support Vector Machines using SMO.

Degree: 2015, University of Georgia

URL: http://hdl.handle.net/10724/31411

► We present an algorithm for Support Vector Machines that can be parallelized effectively. The Algorithm scales up nicely on very large datasets of million training… (more)

Subjects/Keywords: Parallel support vector machine; Sequential minimal optimization

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APA (6^th Edition):

Fatehpuria, N. (2015). Parallel Support Vector Machines using SMO. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/31411

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Fatehpuria, Naman. “Parallel Support Vector Machines using SMO.” 2015. Thesis, University of Georgia. Accessed March 06, 2021. http://hdl.handle.net/10724/31411.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Fatehpuria, Naman. “Parallel Support Vector Machines using SMO.” 2015. Web. 06 Mar 2021.

Vancouver:

Fatehpuria N. Parallel Support Vector Machines using SMO. [Internet] [Thesis]. University of Georgia; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10724/31411.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Fatehpuria N. Parallel Support Vector Machines using SMO. [Thesis]. University of Georgia; 2015. Available from: http://hdl.handle.net/10724/31411

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Anna University

11. Sandra Johnson. Machine learning based approaches to predict and optimize hot methods;.

Degree: 2013, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/13447

►

Just-in-time compilers in a virtual machine environment generate machine code for a method in a program, when the method is first called for execution, incurring… (more)

Subjects/Keywords: Machine learning algorithm; support vector machine; Low Level Virtual Machine

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APA (6^th Edition):

Johnson, S. (2013). Machine learning based approaches to predict and optimize hot methods;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/13447

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Johnson, Sandra. “Machine learning based approaches to predict and optimize hot methods;.” 2013. Thesis, Anna University. Accessed March 06, 2021. http://shodhganga.inflibnet.ac.in/handle/10603/13447.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Johnson, Sandra. “Machine learning based approaches to predict and optimize hot methods;.” 2013. Web. 06 Mar 2021.

Vancouver:

Johnson S. Machine learning based approaches to predict and optimize hot methods;. [Internet] [Thesis]. Anna University; 2013. [cited 2021 Mar 06]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/13447.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Johnson S. Machine learning based approaches to predict and optimize hot methods;. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/13447

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Indian Institute of Science

12. Asharaf, S. Efficient Kernel Methods For Large Scale Classification.

Degree: PhD, Faculty of Engineering, 2011, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/1076

► Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms… (more)

▼ Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms that scale well with the size of the data sets involved. A classification algorithm is said to be scalable if there is no significant increase in time and space requirements for the algorithm (without compromising the generalization performance) when dealing with an increase in the training set size. Support Vector Machine (SVM) is one of the most celebrated kernel based classification methods used in Machine Learning. An SVM capable of handling large scale classification problems will definitely be an ideal candidate in many real world applications. The training process involved in SVM classifier is usually formulated as a Quadratic Programing(QP) problem. The existing solution strategies for this problem have an associated time and space complexity that is (at least) quadratic in the number of training points. This makes the SVM training very expensive even on classification problems having a few thousands of training examples. This thesis addresses the scalability of the training algorithms involved in both two class and multiclass Support Vector Machines. Efficient training schemes reducing the space and time requirements of the SVM training process are proposed as possible solutions. The classification schemes discussed in the thesis for handling large scale two class classification problems are a) Two selective sampling based training schemes for scaling Non-linear SVM and b) Clustering based approaches for handling unbalanced data sets with Core Vector Machine. To handle large scale multicalss classification problems, the thesis proposes Multiclass Core Vector Machine (MCVM), a scalable SVM based multiclass classifier. In MVCM, the multiclass SVM problem is shown to be equivalent to a Minimum Enclosing Ball (MEB) problem and is then solved using a fast approximate MEB finding algorithm. Experimental studies were done with several large real world data sets such as IJCNN1 and Acoustic data sets from LIBSVM page, Extended USPS data set from CVM page and network intrusion detection data sets of DARPA, US Defense used in KDD 99 contest. From the empirical results it is observed that the proposed classification schemes achieve good generalization performance at low time and space requirements. Further, the scalability experiments done with large training data sets have demonstrated that the proposed schemes scale well. A novel soft clustering scheme called Rough Support Vector Clustering (RSVC) employing the idea of Soft Minimum Enclosing Ball Problem (SMEB) is another contribution discussed in this thesis. Experiments done with a synthetic data set and the real world data set namely IRIS, have shown that RSVC finds meaningful soft cluster abstractions. Advisors/Committee Members: Narasimha Murty, M (advisor).

Subjects/Keywords: Machine Learning; Automatic Classification; Kernel Method; Classification Algorithms; Support Vector Machine (SVM); Core Vector Machine (CVM); Rough Support Vector Clustering (RSVC); Multiclass Core Vector Machine (MCVM); Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Asharaf, S. (2011). Efficient Kernel Methods For Large Scale Classification. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/1076

Chicago Manual of Style (16^th Edition):

Asharaf, S. “Efficient Kernel Methods For Large Scale Classification.” 2011. Doctoral Dissertation, Indian Institute of Science. Accessed March 06, 2021. http://etd.iisc.ac.in/handle/2005/1076.

MLA Handbook (7^th Edition):

Asharaf, S. “Efficient Kernel Methods For Large Scale Classification.” 2011. Web. 06 Mar 2021.

Vancouver:

Asharaf S. Efficient Kernel Methods For Large Scale Classification. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2011. [cited 2021 Mar 06]. Available from: http://etd.iisc.ac.in/handle/2005/1076.

Council of Science Editors:

Asharaf S. Efficient Kernel Methods For Large Scale Classification. [Doctoral Dissertation]. Indian Institute of Science; 2011. Available from: http://etd.iisc.ac.in/handle/2005/1076

NSYSU

13. Chang, Chia-Hung. Accuracy Improvement for RNA Secondary Structure Prediction with SVM.

Degree: Master, Computer Science and Engineering, 2008, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319

► Ribonucleic acid (RNA) sometimes occurs in a complex structure called pseudoknots. Prediction of RNA secondary structures has drawn much attention from both biologists and computer… (more)

Subjects/Keywords: RNA; secondary structure; support vector machine; machine learning; classification

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APA (6^th Edition):

Chang, C. (2008). Accuracy Improvement for RNA Secondary Structure Prediction with SVM. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chang, Chia-Hung. “Accuracy Improvement for RNA Secondary Structure Prediction with SVM.” 2008. Thesis, NSYSU. Accessed March 06, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chang, Chia-Hung. “Accuracy Improvement for RNA Secondary Structure Prediction with SVM.” 2008. Web. 06 Mar 2021.

Vancouver:

Chang C. Accuracy Improvement for RNA Secondary Structure Prediction with SVM. [Internet] [Thesis]. NSYSU; 2008. [cited 2021 Mar 06]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chang C. Accuracy Improvement for RNA Secondary Structure Prediction with SVM. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730108-234319

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rochester Institute of Technology

14. Karvekar, Swapnali Babasaheb. Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis.

Degree: MS, Industrial and Systems Engineering, 2019, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/10275

► Human fatigue due to repetitive and physically challenging jobs may result in poor performance and a Work-related Musculoskeletal Disorder (WMSD). Thus, the importance of… (more)

Subjects/Keywords: Human muscle fatigue; Machine learning; Smartphone; Support vector machine; Wearable technology

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APA (6^th Edition):

Karvekar, S. B. (2019). Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10275

Chicago Manual of Style (16^th Edition):

Karvekar, Swapnali Babasaheb. “Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed March 06, 2021. https://scholarworks.rit.edu/theses/10275.

MLA Handbook (7^th Edition):

Karvekar, Swapnali Babasaheb. “Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis.” 2019. Web. 06 Mar 2021.

Vancouver:

Karvekar SB. Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Mar 06]. Available from: https://scholarworks.rit.edu/theses/10275.

Council of Science Editors:

Karvekar SB. Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10275

University of Edinburgh

15. Zarogianni, Eleni. Machine learning and brain imaging in psychosis.

Degree: PhD, 2016, University of Edinburgh

URL: http://hdl.handle.net/1842/22814

► Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and… (more)

▼ Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and treat psychosis prior to fulfilling diagnostic criteria for the disorder. To this aim, reliable early diagnostic biomarkers are needed in order to identify a high-risk state for psychosis and also predict transition to frank psychosis in those high-risk individuals destined to develop the disorder. Recently, machine learning methods have been successfully applied in the diagnostic classification of schizophrenia and in predicting transition to psychosis at an individual level based on magnetic resonance imaging (MRI) data and also neurocognitive variables. This work investigates the application of machine learning methods for the early identification of schizophrenia in subjects at high risk for developing the disorder. The dataset used in this work involves data from the Edinburgh High Risk Study (EHRS), which examined individuals at a heightened risk for developing schizophrenia for familial reasons, and the FePsy (Fruherkennung von Psychosen) study that was conducted in Basel and involves subjects at a clinical high-risk state for psychosis. The overriding aim of this thesis was to use machine learning, and specifically Support Vector Machine (SVM), in order to identify predictors of transition to psychosis in high-risk individuals, using baseline structural MRI data. There are three aims pertaining to this main one. (i) Firstly, our aim was to examine the feasibility of distinguishing at baseline those individuals who later developed schizophrenia from those who did not, yet had psychotic symptoms using SVM and baseline data from the EHRS study. (ii) Secondly, we intended to examine if our classification approach could generalize to clinical high-risk cohorts, using neuroanatomical data from the FePsy study. (iii) In a more exploratory context, we have also examined the diagnostic performance of our classifier by pooling the two datasets together. With regards to the first aim, our findings suggest that the early prediction of schizophrenia is feasible using a MRI-based linear SVM classifier operating at the single-subject level. Additionally, we have shown that the combination of baseline neuroanatomical data with measures of neurocognitive functioning and schizotypal cognition can improve predictive performance. The application of our pattern classification approach to baseline structural MRI data from the FePsy study highly replicated our previous findings. Our classification method identified spatially distributed networks that discriminate at baseline between subjects that later developed schizophrenia and other related psychoses and those that did not. Finally, a preliminary classification analysis using pooled datasets from the EHRS and the FePsy study supports the existence of a neuroanatomical pattern that differentiates between groups of high-risk subjects that develop psychosis against those who do not across research sites and…

Subjects/Keywords: 616.89; machine learning; support vector machine; SVM; MRI; schizophrenia

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APA (6^th Edition):

Zarogianni, E. (2016). Machine learning and brain imaging in psychosis. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/22814

Chicago Manual of Style (16^th Edition):

Zarogianni, Eleni. “Machine learning and brain imaging in psychosis.” 2016. Doctoral Dissertation, University of Edinburgh. Accessed March 06, 2021. http://hdl.handle.net/1842/22814.

MLA Handbook (7^th Edition):

Zarogianni, Eleni. “Machine learning and brain imaging in psychosis.” 2016. Web. 06 Mar 2021.

Vancouver:

Zarogianni E. Machine learning and brain imaging in psychosis. [Internet] [Doctoral dissertation]. University of Edinburgh; 2016. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/1842/22814.

Council of Science Editors:

Zarogianni E. Machine learning and brain imaging in psychosis. [Doctoral Dissertation]. University of Edinburgh; 2016. Available from: http://hdl.handle.net/1842/22814

University of Alberta

16. Bostan, Babak. Predicting homologous signaling pathways using machine learning.

Degree: MS, Department of Computing Science, 2009, University of Alberta

URL: https://era.library.ualberta.ca/files/765372669

► Understanding biochemical reactions inside cells of individual organisms is a key factor for improving our biological knowledge. Signaling pathways provide a road map for a… (more)

Subjects/Keywords: signaling pathway; machine learning; prediction; support vector machine

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APA (6^th Edition):

Bostan, B. (2009). Predicting homologous signaling pathways using machine learning. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/765372669

Chicago Manual of Style (16^th Edition):

Bostan, Babak. “Predicting homologous signaling pathways using machine learning.” 2009. Masters Thesis, University of Alberta. Accessed March 06, 2021. https://era.library.ualberta.ca/files/765372669.

MLA Handbook (7^th Edition):

Bostan, Babak. “Predicting homologous signaling pathways using machine learning.” 2009. Web. 06 Mar 2021.

Vancouver:

Bostan B. Predicting homologous signaling pathways using machine learning. [Internet] [Masters thesis]. University of Alberta; 2009. [cited 2021 Mar 06]. Available from: https://era.library.ualberta.ca/files/765372669.

Council of Science Editors:

Bostan B. Predicting homologous signaling pathways using machine learning. [Masters Thesis]. University of Alberta; 2009. Available from: https://era.library.ualberta.ca/files/765372669

Universidade Nova

17. Camelo, Pedro Miguel Bairrão de Seixas. Botnet cluster identification.

Degree: 2015, Universidade Nova

URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14181

► Botnets are a group of computers infected with a specific sub-set of a malware family and controlled by one individual, called botmaster. This kind of… (more)

Subjects/Keywords: Cibersecurity; Botnet; Malware; Machine learning; Clustering; Support vector machine

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APA (6^th Edition):

Camelo, P. M. B. d. S. (2015). Botnet cluster identification. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14181

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Camelo, Pedro Miguel Bairrão de Seixas. “Botnet cluster identification.” 2015. Thesis, Universidade Nova. Accessed March 06, 2021. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14181.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Camelo, Pedro Miguel Bairrão de Seixas. “Botnet cluster identification.” 2015. Web. 06 Mar 2021.

Vancouver:

Camelo PMBdS. Botnet cluster identification. [Internet] [Thesis]. Universidade Nova; 2015. [cited 2021 Mar 06]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14181.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Camelo PMBdS. Botnet cluster identification. [Thesis]. Universidade Nova; 2015. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14181

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Colorado State University

18. Rogers, Mark F. From RNA-Seq to gene annotation using the splicegrapher method.

Degree: PhD, Computer Science, 2013, Colorado State University

URL: http://hdl.handle.net/10217/80971

► Messenger RNA (mRNA) plays a central role in carrying out the instructions encoded in a gene. A gene's components may be combined in various ways… (more)

▼ Messenger RNA (mRNA) plays a central role in carrying out the instructions encoded in a gene. A gene's components may be combined in various ways to generate a diverse range of mRNA molecules, or transcripts, through a process called alternative splicing (AS). This allows each gene to produce different products under different conditions, such as different stages of development or in different tissues. Researchers can study the diverse set of transcripts a gene produces by sequencing its mRNA. The latest sequencing technology produces millions of short sequence reads (RNA-Seq) from mRNA transcripts, providing researchers with unprecedented opportunities to assess how genetic instructions change under different conditions. It is relatively inexpensive and easy to obtain these reads, but one limitation has been the lack of versatile methods to analyze the data. Most methods attempt to predict complete mRNA transcripts from patterns of RNA-Seq reads ascribed to a particular gene, but the short length of these reads makes transcript prediction problematic. We present a method, called SpliceGrapherXT, that takes a different approach by predicting splice graphs that capture in a single structure all the ways in which a gene's components may be assembled. Whereas other methods make predictions primarily from RNA-Seq evidence, SpliceGrapherXT uses gene annotations describing known transcripts to guide its predictions. We show that this approach allows SpliceGrapherXT to make predictions that encapsulate gene architectures more accurately than other state-of-the-art methods. This accuracy is crucial not only for updating gene annotations, but our splice graph predictions can contribute to more accurate transcript predictions as well. Finally we demonstrate that by using SpliceGrapherXT to assess AS on a genome-wide scale, we can gain new insights into the ways that specific genes and environmental conditions may impact an organism's transcriptome. SpliceGrapherXT is available for download at http://splicegrapher.sourceforge.net. Advisors/Committee Members: Ben-Hur, Asa (advisor), Boucher, Christina (committee member), Anderson, Charles (committee member), Reddy, Anireddy S. N. (committee member).

Subjects/Keywords: RNA-seq; machine learning; support vector machine; computational biology; inference

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APA (6^th Edition):

Rogers, M. F. (2013). From RNA-Seq to gene annotation using the splicegrapher method. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/80971

Chicago Manual of Style (16^th Edition):

Rogers, Mark F. “From RNA-Seq to gene annotation using the splicegrapher method.” 2013. Doctoral Dissertation, Colorado State University. Accessed March 06, 2021. http://hdl.handle.net/10217/80971.

MLA Handbook (7^th Edition):

Rogers, Mark F. “From RNA-Seq to gene annotation using the splicegrapher method.” 2013. Web. 06 Mar 2021.

Vancouver:

Rogers MF. From RNA-Seq to gene annotation using the splicegrapher method. [Internet] [Doctoral dissertation]. Colorado State University; 2013. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10217/80971.

Council of Science Editors:

Rogers MF. From RNA-Seq to gene annotation using the splicegrapher method. [Doctoral Dissertation]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/80971

Tampere University

19. Kallio, Julius. Support Vector Machine and Deep Learning in Medical Applications .

Degree: 2017, Tampere University

URL: https://trepo.tuni.fi/handle/10024/101911

► Deep learning is a new field in machine learning that focuses mainly on deep neural networks. In this thesis, we focus on machine learning and… (more)

Subjects/Keywords: deep learning; machine learning; neural networks; support vector machine

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APA (6^th Edition):

Kallio, J. (2017). Support Vector Machine and Deep Learning in Medical Applications . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/101911

Chicago Manual of Style (16^th Edition):

Kallio, Julius. “Support Vector Machine and Deep Learning in Medical Applications .” 2017. Masters Thesis, Tampere University. Accessed March 06, 2021. https://trepo.tuni.fi/handle/10024/101911.

MLA Handbook (7^th Edition):

Kallio, Julius. “Support Vector Machine and Deep Learning in Medical Applications .” 2017. Web. 06 Mar 2021.

Vancouver:

Kallio J. Support Vector Machine and Deep Learning in Medical Applications . [Internet] [Masters thesis]. Tampere University; 2017. [cited 2021 Mar 06]. Available from: https://trepo.tuni.fi/handle/10024/101911.

Council of Science Editors:

Kallio J. Support Vector Machine and Deep Learning in Medical Applications . [Masters Thesis]. Tampere University; 2017. Available from: https://trepo.tuni.fi/handle/10024/101911

Delft University of Technology

20. Kharagjitsing, Manish (author). Optimizing Support Vector Machines with ISBA-A-gs Land Surface Variables as a Surrogate Model to Simulate ASCAT Derived Parameters.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:2a9c3314-57b6-4471-9d81-b79199e5ffbd

►

The TU-Wien developed a soil moisture retrieval algorithm that uses the incidence angle dependence of backscatter to obtain soil moisture estimates (Wagner et al., 1999).… (more)

▼

The TU-Wien developed a soil moisture retrieval algorithm that uses the incidence angle dependence of backscatter to obtain soil moisture estimates (Wagner et al., 1999). The core of this algorithm is a second order Taylor expansion with which the backscatter is normalized at a reference angle. Studies have shown that the first and second order derivative within this Taylor expansion, known as slope and curvature, are somehow related to the wet biomass and structure of vegetation. The general approach to forward model satellite observations with land surface variables in a data assimilation framework is through a radiative transfer model (Albergel et al., 2017). However, this requires plenty of assumptions about the vegetation canopy (such as stem height, shape, size, orientation etc.) and is therefore relatively inefficient for understanding the impact of soil moisture and vegetation dynamics on backscatter on a large scale. This study investigates the possibility of using support vector machines as a surrogate model instead of a radiative transfer model to link the TU-Wien normalized backscatter and slope to land surface variables soil moisture and leaf area index. The land surface variables are simulations from the CO2-responsive ISBA-A-gs land surface model. Support vector machines have the advantage of providing implicit kernel functions, which make them very useful for non-linear problems. The ISBA-A-gs data is provided by Météo-France. In total, 1324 support vector machines have been optimized through a cross validated grid search. The optimized hyperparameters were shown to have spatial consistency and look promising as an initial approach to forward modelling backscatter and slope. The SVM performances are further investigated through corresponding land cover types of grid points and the land surface variables.

Geoscience and Remote Sensing

Advisors/Committee Members: Lhermitte, Stef (mentor), Steele-Dunne, Susan (graduation committee), Coenders-Gerrits, Miriam (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: ASCAT; Remote Sensing; Machine Learning; Support Vector Machine

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APA (6^th Edition):

Kharagjitsing, M. (. (2020). Optimizing Support Vector Machines with ISBA-A-gs Land Surface Variables as a Surrogate Model to Simulate ASCAT Derived Parameters. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:2a9c3314-57b6-4471-9d81-b79199e5ffbd

Chicago Manual of Style (16^th Edition):

Kharagjitsing, Manish (author). “Optimizing Support Vector Machines with ISBA-A-gs Land Surface Variables as a Surrogate Model to Simulate ASCAT Derived Parameters.” 2020. Masters Thesis, Delft University of Technology. Accessed March 06, 2021. http://resolver.tudelft.nl/uuid:2a9c3314-57b6-4471-9d81-b79199e5ffbd.

MLA Handbook (7^th Edition):

Kharagjitsing, Manish (author). “Optimizing Support Vector Machines with ISBA-A-gs Land Surface Variables as a Surrogate Model to Simulate ASCAT Derived Parameters.” 2020. Web. 06 Mar 2021.

Vancouver:

Kharagjitsing M(. Optimizing Support Vector Machines with ISBA-A-gs Land Surface Variables as a Surrogate Model to Simulate ASCAT Derived Parameters. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 06]. Available from: http://resolver.tudelft.nl/uuid:2a9c3314-57b6-4471-9d81-b79199e5ffbd.

Council of Science Editors:

Kharagjitsing M(. Optimizing Support Vector Machines with ISBA-A-gs Land Surface Variables as a Surrogate Model to Simulate ASCAT Derived Parameters. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:2a9c3314-57b6-4471-9d81-b79199e5ffbd

Addis Ababa University

21. Haftom, Aregawi. Weather Forecasting using Deep Learning Algorithm for the Ethiopian Context .

Degree: 2018, Addis Ababa University

URL: http://10.6.20.92/handle/123456789/20925

► Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time and a given location. Now… (more)

Subjects/Keywords: Weather; Deep Belief Network; Support Vector Machine; Restricted Boltzman Machine; Meteorology

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APA (6^th Edition):

Haftom, A. (2018). Weather Forecasting using Deep Learning Algorithm for the Ethiopian Context . (Thesis). Addis Ababa University. Retrieved from http://10.6.20.92/handle/123456789/20925

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Haftom, Aregawi. “Weather Forecasting using Deep Learning Algorithm for the Ethiopian Context .” 2018. Thesis, Addis Ababa University. Accessed March 06, 2021. http://10.6.20.92/handle/123456789/20925.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Haftom, Aregawi. “Weather Forecasting using Deep Learning Algorithm for the Ethiopian Context .” 2018. Web. 06 Mar 2021.

Vancouver:

Haftom A. Weather Forecasting using Deep Learning Algorithm for the Ethiopian Context . [Internet] [Thesis]. Addis Ababa University; 2018. [cited 2021 Mar 06]. Available from: http://10.6.20.92/handle/123456789/20925.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Haftom A. Weather Forecasting using Deep Learning Algorithm for the Ethiopian Context . [Thesis]. Addis Ababa University; 2018. Available from: http://10.6.20.92/handle/123456789/20925

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

22. McCurry, Katherine Lorraine. The Development and Validation of a Neural Model of Affective States.

Degree: MS, Psychology, 2015, Virginia Tech

URL: http://hdl.handle.net/10919/78166

► Emotion dysregulation plays a central role in psychopathology (B. Bradley et al., 2011) and has been linked to aberrant activation of neural circuitry involved in… (more)

▼ Emotion dysregulation plays a central role in psychopathology (B. Bradley et al., 2011) and has been linked to aberrant activation of neural circuitry involved in emotion regulation (Beauregard, Paquette, & Lévesque, 2006; Etkin & Schatzberg, 2011). In recent years, technological advances in neuroimaging methods coupled with developments in machine learning have allowed for the non-invasive measurement and prediction of brain states in real-time, which can be used to provide feedback to facilitate regulation of brain states (LaConte, 2011). Real-time functional magnetic resonance imaging (rt-fMRI)-guided neurofeedback, has promise as a novel therapeutic method in which individuals are provided with tailored feedback to improve regulation of emotional responses (Stoeckel et al., 2014). However, effective use of this technology for such purposes likely entails the development of (a) a normative model of emotion processing to provide feedback for individuals with emotion processing difficulties; and (b) best practices concerning how these types of group models are designed and translated for use in a rt-fMRI environment (Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014). To this end, the present study utilized fMRI data from a standard emotion elicitation paradigm to examine the impact of several design decisions made during the development of a whole-brain model of affective processing. Using support vector machine (SVM) learning, we developed a group model that reliably classified brain states associated with passive viewing of positive, negative, and neutral images. After validating the group whole-brain model, we adapted this model for use in an rt-fMRI experiment, and using a second imaging dataset along with our group model, we simulated rt-fMRI predictions and tested options for providing feedback. Advisors/Committee Members: King-Casas, Brooks (committeechair), White, Susan W. (committee member), LaConte, Stephen M. (committee member), Chiu, Pearl H. (committee member).

Subjects/Keywords: machine learning; neurofeedback; fMRI; emotion; support vector machine

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APA (6^th Edition):

McCurry, K. L. (2015). The Development and Validation of a Neural Model of Affective States. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78166

Chicago Manual of Style (16^th Edition):

McCurry, Katherine Lorraine. “The Development and Validation of a Neural Model of Affective States.” 2015. Masters Thesis, Virginia Tech. Accessed March 06, 2021. http://hdl.handle.net/10919/78166.

MLA Handbook (7^th Edition):

McCurry, Katherine Lorraine. “The Development and Validation of a Neural Model of Affective States.” 2015. Web. 06 Mar 2021.

Vancouver:

McCurry KL. The Development and Validation of a Neural Model of Affective States. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10919/78166.

Council of Science Editors:

McCurry KL. The Development and Validation of a Neural Model of Affective States. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/78166

NSYSU

23. Su, Wen-pin. Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition.

Degree: Master, Electrical Engineering, 2008, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006

► Finding an efficient method to detect counterfeit banknotes is imperative. In this study, we propose multiple kernel weighted support vector machine for counterfeit banknote recognition.… (more)

Subjects/Keywords: Support vector machine; Banknote recognition; Multiple kernel learning; Semidefinite programming; False alarm rate; Weighted support vector machine

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Su, W. (2008). Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Su, Wen-pin. “Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition.” 2008. Thesis, NSYSU. Accessed March 06, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Su, Wen-pin. “Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition.” 2008. Web. 06 Mar 2021.

Vancouver:

Su W. Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition. [Internet] [Thesis]. NSYSU; 2008. [cited 2021 Mar 06]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Su W. Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Otago

24. Woods, Alan Michael. Diﬀerentiation of Parkinson’s Tremor from Essential Tremor using Action Tremor Analysis .

Degree: 2012, University of Otago

URL: http://hdl.handle.net/10523/2513

► Similarities in the physiological and psychological symptoms of Parkinson’s disease (PD) and Essential Tremor (ET) make accurate diagnosis of PD and ET conditions difficult. It… (more)

▼ Similarities in the physiological and psychological symptoms of Parkinson’s disease (PD) and Essential Tremor (ET) make accurate diagnosis of PD and ET conditions difficult. It has up to 25% diagnostic error rate. Both disorders have similar postural tremor characteristics, which make it difficult to differentiate on the basis of tremor between the two disorders. Previous studies that have classified PD and ET tremor used multiple neural learning tools and decision support systems to classify between the two conditions. In contrast, we explored the use of Discrete Wavelet Transforms combined with Support Vector Machines and changes in cognitive load as a disorder classification method. Advancements in mobile device technology allow these devices to be used as mobile medical applications. This provides the opportunity for one device to collect, analyse and classify biometric data from a range of disorders. Biometric data has been collected by mobile devices for a number of years, but most analysis and classification have been performed off-line on a central server. In this research, we have concentrated on two questions: Can changes in tremor be used to classify PD and ET postural tremor on a mobile phone? Do the effects of changes in cognitive load through differing levels of attention and distraction affect the level and frequency of tremor differently in PD and ET disorders? We investigate the influence of attention and distraction on tremor by comparingParkinson’s postural tremor with Essential postural tremor as the between-subjects factor and differing attention and distraction levels as within-subjects factors. A primary finding is that in the frequency band directly related to postural tremor in both Essential Tremor and Parkinson’s Disease there is significant difference in the disorders between attention and distraction tasks. These findings suggest that attention and distraction can be successfully used as an input feature space to classify difference in these two disorders. Using the differences between attention and distraction tasks we successfully developed a proof-of-concept smartphone based mobile medical application using discrete wavelet transforms and support vector machine based classification to discriminate between Parkinson’s postural tremor and essential postural tremor. Keywords: Parkinson Disease; Essential Tremor; Wavelet; Support Vector Machine; Attention; Distraction Advisors/Committee Members: Nowostawski , Mariusz (advisor).

Subjects/Keywords: Parkinson Disease; Essential Tremor; Wavelet; Support Vector Machine; Attention; Distraction; Parkinson Disease; Essential Tremor; Wavelet; Support Vector Machine; Attention; Distraction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Woods, A. M. (2012). Diﬀerentiation of Parkinson’s Tremor from Essential Tremor using Action Tremor Analysis . (Masters Thesis). University of Otago. Retrieved from http://hdl.handle.net/10523/2513

Chicago Manual of Style (16^th Edition):

Woods, Alan Michael. “Diﬀerentiation of Parkinson’s Tremor from Essential Tremor using Action Tremor Analysis .” 2012. Masters Thesis, University of Otago. Accessed March 06, 2021. http://hdl.handle.net/10523/2513.

MLA Handbook (7^th Edition):

Woods, Alan Michael. “Diﬀerentiation of Parkinson’s Tremor from Essential Tremor using Action Tremor Analysis .” 2012. Web. 06 Mar 2021.

Vancouver:

Woods AM. Diﬀerentiation of Parkinson’s Tremor from Essential Tremor using Action Tremor Analysis . [Internet] [Masters thesis]. University of Otago; 2012. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10523/2513.

Council of Science Editors:

Woods AM. Diﬀerentiation of Parkinson’s Tremor from Essential Tremor using Action Tremor Analysis . [Masters Thesis]. University of Otago; 2012. Available from: http://hdl.handle.net/10523/2513

Brno University of Technology

25. Hrabinová, Barbora. Vytvoření předpovědi průměrných měsíčních průtoků pro řízení zásobní funkce fiktivní vodní nádrže: Creating predictions average monthly flow for the control of the storage capacity of a fictive reservoir dam.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/70652

► The diploma thesis is focused on predictions of mean monthly flows for a purpose of control of storage functions when thinking differently positions of fictive… (more)

Subjects/Keywords: předpověď; průměrné měsíční průtoky; Support vector machine; simulace; vodní nádrž; Prediction; Mean Monthly Flow; Support Vector Machine; Simulation; Reservoir

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hrabinová, B. (2019). Vytvoření předpovědi průměrných měsíčních průtoků pro řízení zásobní funkce fiktivní vodní nádrže: Creating predictions average monthly flow for the control of the storage capacity of a fictive reservoir dam. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/70652

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hrabinová, Barbora. “Vytvoření předpovědi průměrných měsíčních průtoků pro řízení zásobní funkce fiktivní vodní nádrže: Creating predictions average monthly flow for the control of the storage capacity of a fictive reservoir dam.” 2019. Thesis, Brno University of Technology. Accessed March 06, 2021. http://hdl.handle.net/11012/70652.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hrabinová, Barbora. “Vytvoření předpovědi průměrných měsíčních průtoků pro řízení zásobní funkce fiktivní vodní nádrže: Creating predictions average monthly flow for the control of the storage capacity of a fictive reservoir dam.” 2019. Web. 06 Mar 2021.

Vancouver:

Hrabinová B. Vytvoření předpovědi průměrných měsíčních průtoků pro řízení zásobní funkce fiktivní vodní nádrže: Creating predictions average monthly flow for the control of the storage capacity of a fictive reservoir dam. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/11012/70652.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hrabinová B. Vytvoření předpovědi průměrných měsíčních průtoků pro řízení zásobní funkce fiktivní vodní nádrže: Creating predictions average monthly flow for the control of the storage capacity of a fictive reservoir dam. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/70652

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

26. Hrabinová, Barbora. Vytvoření předpovědí průměrných měsíčních průtoků pro strategické řízení vodní nádrže: Creating predictions average monthly flow for the strategic control of water reservoir.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/62666

► The bachelor`s thesis is focused on predictions of mean monthly flows for a purpose of control of reservoir Vír I. Predictions are made by Support… (more)

Subjects/Keywords: předpověď; průměrné měsíční průtoky; Support vector machine; simulace; vodní nádrž; Prediction; Mean Monthly Flow; Support Vector Machine; Simulation; Reservoir

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hrabinová, B. (2020). Vytvoření předpovědí průměrných měsíčních průtoků pro strategické řízení vodní nádrže: Creating predictions average monthly flow for the strategic control of water reservoir. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/62666

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hrabinová, Barbora. “Vytvoření předpovědí průměrných měsíčních průtoků pro strategické řízení vodní nádrže: Creating predictions average monthly flow for the strategic control of water reservoir.” 2020. Thesis, Brno University of Technology. Accessed March 06, 2021. http://hdl.handle.net/11012/62666.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hrabinová, Barbora. “Vytvoření předpovědí průměrných měsíčních průtoků pro strategické řízení vodní nádrže: Creating predictions average monthly flow for the strategic control of water reservoir.” 2020. Web. 06 Mar 2021.

Vancouver:

Hrabinová B. Vytvoření předpovědí průměrných měsíčních průtoků pro strategické řízení vodní nádrže: Creating predictions average monthly flow for the strategic control of water reservoir. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/11012/62666.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hrabinová B. Vytvoření předpovědí průměrných měsíčních průtoků pro strategické řízení vodní nádrže: Creating predictions average monthly flow for the strategic control of water reservoir. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/62666

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

27. Chen, Chia-ying. Speech Emotion Recognition Using Factor Analysis and Identity Vectors.

Degree: Master, Computer Science and Engineering, 2016, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0809116-110726

► In this paper, we challenge INTERSPEECH 2009 Emotion open performance sub-Challenge of 5 class problem. Our research evaluates on the well-known FAU Aibo database. We… (more)

Subjects/Keywords: i-vector; Speech Emotion Recognition; Vocal Tract Length Normalization; Gaussian Mixture Model; Support Vector Machine

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, C. (2016). Speech Emotion Recognition Using Factor Analysis and Identity Vectors. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0809116-110726

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Chia-ying. “Speech Emotion Recognition Using Factor Analysis and Identity Vectors.” 2016. Thesis, NSYSU. Accessed March 06, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0809116-110726.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Chia-ying. “Speech Emotion Recognition Using Factor Analysis and Identity Vectors.” 2016. Web. 06 Mar 2021.

Vancouver:

Chen C. Speech Emotion Recognition Using Factor Analysis and Identity Vectors. [Internet] [Thesis]. NSYSU; 2016. [cited 2021 Mar 06]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0809116-110726.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen C. Speech Emotion Recognition Using Factor Analysis and Identity Vectors. [Thesis]. NSYSU; 2016. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0809116-110726

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

28. Tsai, Bo-Jen. Machine learning Classification based on Principal Component Analysis and Multilinear Principal Component Analysis for Study of Cardiology Ultrasound in Left Ventricle.

Degree: Master, Applied Mathematics, 2014, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-155151

► In this work, we study heart diseases related to diastolic and systolic of heart in left ventricle. Following Hung (2012) and Liu (2012), the differences… (more)

Subjects/Keywords: gray-scale value; random forest; regularized discriminant analysis; support vector machine

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tsai, B. (2014). Machine learning Classification based on Principal Component Analysis and Multilinear Principal Component Analysis for Study of Cardiology Ultrasound in Left Ventricle. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-155151

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tsai, Bo-Jen. “Machine learning Classification based on Principal Component Analysis and Multilinear Principal Component Analysis for Study of Cardiology Ultrasound in Left Ventricle.” 2014. Thesis, NSYSU. Accessed March 06, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-155151.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tsai, Bo-Jen. “Machine learning Classification based on Principal Component Analysis and Multilinear Principal Component Analysis for Study of Cardiology Ultrasound in Left Ventricle.” 2014. Web. 06 Mar 2021.

Vancouver:

Tsai B. Machine learning Classification based on Principal Component Analysis and Multilinear Principal Component Analysis for Study of Cardiology Ultrasound in Left Ventricle. [Internet] [Thesis]. NSYSU; 2014. [cited 2021 Mar 06]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-155151.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tsai B. Machine learning Classification based on Principal Component Analysis and Multilinear Principal Component Analysis for Study of Cardiology Ultrasound in Left Ventricle. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-155151

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

29. Wu, Hsin-Fang. Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins.

Degree: Master, Computer Science and Engineering, 2012, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0911112-132025

► Given a protein sequence and the CÎ± coordinates on its backbone, the all-atom protein backbone reconstruction problem (PBRP) is to reconstruct the backbone by its… (more)

Subjects/Keywords: backbone; bioinformatics; standard protein; support vector machine; feature set

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wu, H. (2012). Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0911112-132025

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wu, Hsin-Fang. “Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins.” 2012. Thesis, NSYSU. Accessed March 06, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0911112-132025.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wu, Hsin-Fang. “Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins.” 2012. Web. 06 Mar 2021.

Vancouver:

Wu H. Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins. [Internet] [Thesis]. NSYSU; 2012. [cited 2021 Mar 06]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0911112-132025.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu H. Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0911112-132025

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

30. Chen, Hong-Yu. The Disulfide Connectivity Prediction with Support Vector Machine and Behavior Knowledge Space.

Degree: Master, Computer Science and Engineering, 2012, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-141458

► The disulfide bond in a protein is a single covalent bond formed from the oxidation of two cysteines. It plays an important role in the… (more)

Subjects/Keywords: behavior knowledge space; support vector machine; connectivity pattern; disulfide bond; cysteine

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, H. (2012). The Disulfide Connectivity Prediction with Support Vector Machine and Behavior Knowledge Space. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-141458

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Hong-Yu. “The Disulfide Connectivity Prediction with Support Vector Machine and Behavior Knowledge Space.” 2012. Thesis, NSYSU. Accessed March 06, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-141458.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Hong-Yu. “The Disulfide Connectivity Prediction with Support Vector Machine and Behavior Knowledge Space.” 2012. Web. 06 Mar 2021.

Vancouver:

Chen H. The Disulfide Connectivity Prediction with Support Vector Machine and Behavior Knowledge Space. [Internet] [Thesis]. NSYSU; 2012. [cited 2021 Mar 06]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-141458.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen H. The Disulfide Connectivity Prediction with Support Vector Machine and Behavior Knowledge Space. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-141458

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations