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You searched for +publisher:"University of Waikato" +contributor:("Holmes, Geoffrey"). Showing records 1 – 6 of 6 total matches.

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University of Waikato

1. Ma, Jinjin. Parameter Tuning Using Gaussian Processes .

Degree: 2012, University of Waikato

 Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good… (more)

Subjects/Keywords: Parameter Tunning; Gaussian Process Optimization; Machine Learning

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APA (6th Edition):

Ma, J. (2012). Parameter Tuning Using Gaussian Processes . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/6497

Chicago Manual of Style (16th Edition):

Ma, Jinjin. “Parameter Tuning Using Gaussian Processes .” 2012. Masters Thesis, University of Waikato. Accessed August 23, 2019. http://hdl.handle.net/10289/6497.

MLA Handbook (7th Edition):

Ma, Jinjin. “Parameter Tuning Using Gaussian Processes .” 2012. Web. 23 Aug 2019.

Vancouver:

Ma J. Parameter Tuning Using Gaussian Processes . [Internet] [Masters thesis]. University of Waikato; 2012. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/10289/6497.

Council of Science Editors:

Ma J. Parameter Tuning Using Gaussian Processes . [Masters Thesis]. University of Waikato; 2012. Available from: http://hdl.handle.net/10289/6497


University of Waikato

2. Read, Jesse. Scalable Multi-label Classification .

Degree: 2010, University of Waikato

 Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations… (more)

Subjects/Keywords: multi-label; scalable methods; classification

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APA (6th Edition):

Read, J. (2010). Scalable Multi-label Classification . (Doctoral Dissertation). University of Waikato. Retrieved from http://hdl.handle.net/10289/4645

Chicago Manual of Style (16th Edition):

Read, Jesse. “Scalable Multi-label Classification .” 2010. Doctoral Dissertation, University of Waikato. Accessed August 23, 2019. http://hdl.handle.net/10289/4645.

MLA Handbook (7th Edition):

Read, Jesse. “Scalable Multi-label Classification .” 2010. Web. 23 Aug 2019.

Vancouver:

Read J. Scalable Multi-label Classification . [Internet] [Doctoral dissertation]. University of Waikato; 2010. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/10289/4645.

Council of Science Editors:

Read J. Scalable Multi-label Classification . [Doctoral Dissertation]. University of Waikato; 2010. Available from: http://hdl.handle.net/10289/4645


University of Waikato

3. Mutter, Stefan. Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation .

Degree: 2011, University of Waikato

 Detecting similarity in biological sequences is a key element to understanding the mechanisms of life. Researchers infer potential structural, functional or evolutionary relationships from similarity.… (more)

Subjects/Keywords: Machine Learning; Bioinformatics; Statistical Modelling; Hidden Markov Models; proteins; amino acids; one-class classification; propositionalisation; null model; discriminative learner; generative model; classification

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APA (6th Edition):

Mutter, S. (2011). Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation . (Doctoral Dissertation). University of Waikato. Retrieved from http://hdl.handle.net/10289/5299

Chicago Manual of Style (16th Edition):

Mutter, Stefan. “Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation .” 2011. Doctoral Dissertation, University of Waikato. Accessed August 23, 2019. http://hdl.handle.net/10289/5299.

MLA Handbook (7th Edition):

Mutter, Stefan. “Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation .” 2011. Web. 23 Aug 2019.

Vancouver:

Mutter S. Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation . [Internet] [Doctoral dissertation]. University of Waikato; 2011. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/10289/5299.

Council of Science Editors:

Mutter S. Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation . [Doctoral Dissertation]. University of Waikato; 2011. Available from: http://hdl.handle.net/10289/5299


University of Waikato

4. Hunkin, Paul Wade. Distributed Operating Systems on Wireless Sensor Networks .

Degree: 2017, University of Waikato

 This thesis proposes the use of traditional distributed operating system and distributed systems techniques that are adapted and applied to the wireless sensor network domain.… (more)

Subjects/Keywords: Wireless Sensor Networks; Operating Systems; Distributed Systems

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APA (6th Edition):

Hunkin, P. W. (2017). Distributed Operating Systems on Wireless Sensor Networks . (Doctoral Dissertation). University of Waikato. Retrieved from http://hdl.handle.net/10289/11059

Chicago Manual of Style (16th Edition):

Hunkin, Paul Wade. “Distributed Operating Systems on Wireless Sensor Networks .” 2017. Doctoral Dissertation, University of Waikato. Accessed August 23, 2019. http://hdl.handle.net/10289/11059.

MLA Handbook (7th Edition):

Hunkin, Paul Wade. “Distributed Operating Systems on Wireless Sensor Networks .” 2017. Web. 23 Aug 2019.

Vancouver:

Hunkin PW. Distributed Operating Systems on Wireless Sensor Networks . [Internet] [Doctoral dissertation]. University of Waikato; 2017. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/10289/11059.

Council of Science Editors:

Hunkin PW. Distributed Operating Systems on Wireless Sensor Networks . [Doctoral Dissertation]. University of Waikato; 2017. Available from: http://hdl.handle.net/10289/11059

5. Puurula, Antti. Scalable Text Mining with Sparse Generative Models .

Degree: 2015, University of Waikato

 The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure… (more)

Subjects/Keywords: text mining; graphical models; machine learning; information retrieval; scalable; sparse computation; text classification; multinomial naive bayes

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APA (6th Edition):

Puurula, A. (2015). Scalable Text Mining with Sparse Generative Models . (Doctoral Dissertation). University of Waikato. Retrieved from http://hdl.handle.net/10289/9435

Chicago Manual of Style (16th Edition):

Puurula, Antti. “Scalable Text Mining with Sparse Generative Models .” 2015. Doctoral Dissertation, University of Waikato. Accessed August 23, 2019. http://hdl.handle.net/10289/9435.

MLA Handbook (7th Edition):

Puurula, Antti. “Scalable Text Mining with Sparse Generative Models .” 2015. Web. 23 Aug 2019.

Vancouver:

Puurula A. Scalable Text Mining with Sparse Generative Models . [Internet] [Doctoral dissertation]. University of Waikato; 2015. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/10289/9435.

Council of Science Editors:

Puurula A. Scalable Text Mining with Sparse Generative Models . [Doctoral Dissertation]. University of Waikato; 2015. Available from: http://hdl.handle.net/10289/9435


University of Waikato

6. Tan, Yu Shyang. Reconstructing Data Provenance from Log Files .

Degree: 2017, University of Waikato

 Data provenance describes the derivation history of data, capturing details such as the entities involved and the relationships between entities. Knowledge of data provenance can… (more)

Subjects/Keywords: Data Provenance; Reconstruction; Log Analysis

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APA (6th Edition):

Tan, Y. S. (2017). Reconstructing Data Provenance from Log Files . (Doctoral Dissertation). University of Waikato. Retrieved from http://hdl.handle.net/10289/11388

Chicago Manual of Style (16th Edition):

Tan, Yu Shyang. “Reconstructing Data Provenance from Log Files .” 2017. Doctoral Dissertation, University of Waikato. Accessed August 23, 2019. http://hdl.handle.net/10289/11388.

MLA Handbook (7th Edition):

Tan, Yu Shyang. “Reconstructing Data Provenance from Log Files .” 2017. Web. 23 Aug 2019.

Vancouver:

Tan YS. Reconstructing Data Provenance from Log Files . [Internet] [Doctoral dissertation]. University of Waikato; 2017. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/10289/11388.

Council of Science Editors:

Tan YS. Reconstructing Data Provenance from Log Files . [Doctoral Dissertation]. University of Waikato; 2017. Available from: http://hdl.handle.net/10289/11388

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