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You searched for subject:(transductive learning). Showing records 1 – 11 of 11 total matches.

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EPFL

1. Paratte, Johann. Graph-based Methods for Visualization and Clustering.

Degree: 2017, EPFL

 The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it… (more)

Subjects/Keywords: data science; dimensionality reduction; scalable processing; graph signal processing; sampling; transductive learning; embedding; clustering; visualization

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

APA (6th Edition):

Paratte, J. (2017). Graph-based Methods for Visualization and Clustering. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/231710

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

Chicago Manual of Style (16th Edition):

Paratte, Johann. “Graph-based Methods for Visualization and Clustering.” 2017. Thesis, EPFL. Accessed December 08, 2019. http://infoscience.epfl.ch/record/231710.

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

MLA Handbook (7th Edition):

Paratte, Johann. “Graph-based Methods for Visualization and Clustering.” 2017. Web. 08 Dec 2019.

Vancouver:

Paratte J. Graph-based Methods for Visualization and Clustering. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Dec 08]. Available from: http://infoscience.epfl.ch/record/231710.

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

Council of Science Editors:

Paratte J. Graph-based Methods for Visualization and Clustering. [Thesis]. EPFL; 2017. Available from: http://infoscience.epfl.ch/record/231710

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


Université Catholique de Louvain

2. Ernst, David. Graph-based semi-supervised classification algorithms in light of the recently proposed adaptive edge weighting and the question whether it can be extended to out-of-sample prediction.

Degree: 2017, Université Catholique de Louvain

This master thesis studies how graph-based semi-supervised classification algorithms can be extended to out-of-sample prediction. Two approaches are studied: graph freezing (possible thanks to adaptive… (more)

Subjects/Keywords: machine-learning; algorithms; semi-supervised; graph; classification; adaptive edge weighting; transductive; out-of-sample

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

APA (6th Edition):

Ernst, D. (2017). Graph-based semi-supervised classification algorithms in light of the recently proposed adaptive edge weighting and the question whether it can be extended to out-of-sample prediction. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/thesis:8488

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

Chicago Manual of Style (16th Edition):

Ernst, David. “Graph-based semi-supervised classification algorithms in light of the recently proposed adaptive edge weighting and the question whether it can be extended to out-of-sample prediction.” 2017. Thesis, Université Catholique de Louvain. Accessed December 08, 2019. http://hdl.handle.net/2078.1/thesis:8488.

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

MLA Handbook (7th Edition):

Ernst, David. “Graph-based semi-supervised classification algorithms in light of the recently proposed adaptive edge weighting and the question whether it can be extended to out-of-sample prediction.” 2017. Web. 08 Dec 2019.

Vancouver:

Ernst D. Graph-based semi-supervised classification algorithms in light of the recently proposed adaptive edge weighting and the question whether it can be extended to out-of-sample prediction. [Internet] [Thesis]. Université Catholique de Louvain; 2017. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2078.1/thesis:8488.

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

Council of Science Editors:

Ernst D. Graph-based semi-supervised classification algorithms in light of the recently proposed adaptive edge weighting and the question whether it can be extended to out-of-sample prediction. [Thesis]. Université Catholique de Louvain; 2017. Available from: http://hdl.handle.net/2078.1/thesis:8488

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


Kansas State University

3. Tangirala, Karthik. Semi-supervised and transductive learning algorithms for predicting alternative splicing events in genes.

Degree: MS, Department of Computing and Information Sciences, 2011, Kansas State University

 As genomes are sequenced, a major challenge is their annotation  – the identification of genes and regulatory elements, their locations and their functions. For years,… (more)

Subjects/Keywords: Alternative splicing; Co training; Semi supervised learning; Transductive learning; Graph based approach; Bioinformatics (0715); Computer Science (0984)

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APA (6th Edition):

Tangirala, K. (2011). Semi-supervised and transductive learning algorithms for predicting alternative splicing events in genes. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/12013

Chicago Manual of Style (16th Edition):

Tangirala, Karthik. “Semi-supervised and transductive learning algorithms for predicting alternative splicing events in genes.” 2011. Masters Thesis, Kansas State University. Accessed December 08, 2019. http://hdl.handle.net/2097/12013.

MLA Handbook (7th Edition):

Tangirala, Karthik. “Semi-supervised and transductive learning algorithms for predicting alternative splicing events in genes.” 2011. Web. 08 Dec 2019.

Vancouver:

Tangirala K. Semi-supervised and transductive learning algorithms for predicting alternative splicing events in genes. [Internet] [Masters thesis]. Kansas State University; 2011. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2097/12013.

Council of Science Editors:

Tangirala K. Semi-supervised and transductive learning algorithms for predicting alternative splicing events in genes. [Masters Thesis]. Kansas State University; 2011. Available from: http://hdl.handle.net/2097/12013

4. F. Vitale. FAST LEARNING ON GRAPHS.

Degree: 2011, Università degli Studi di Milano

 We carry out a systematic study of classification problems on networked data, presenting novel techniques with good performance both in theory and in practice. We… (more)

Subjects/Keywords: graph learning; graph prediction; graph theory; graph clustering; transductive learning; online learning; random spanning trees; random walks; node classification; effective resistance; Settore INF/01 - Informatica

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APA (6th Edition):

Vitale, F. (2011). FAST LEARNING ON GRAPHS. (Thesis). Università degli Studi di Milano. Retrieved from http://hdl.handle.net/2434/155500

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

Chicago Manual of Style (16th Edition):

Vitale, F.. “FAST LEARNING ON GRAPHS.” 2011. Thesis, Università degli Studi di Milano. Accessed December 08, 2019. http://hdl.handle.net/2434/155500.

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

MLA Handbook (7th Edition):

Vitale, F.. “FAST LEARNING ON GRAPHS.” 2011. Web. 08 Dec 2019.

Vancouver:

Vitale F. FAST LEARNING ON GRAPHS. [Internet] [Thesis]. Università degli Studi di Milano; 2011. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2434/155500.

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

Council of Science Editors:

Vitale F. FAST LEARNING ON GRAPHS. [Thesis]. Università degli Studi di Milano; 2011. Available from: http://hdl.handle.net/2434/155500

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

5. Döngel, Tuğçe. Büyük imge veri tabanlarında erişim için yarı eğitmenli görsel temsiller .

Degree: ESOGÜ, Mühendislik Mimarlık Fakültesi, Elektrik-Elektronik Mühendisliği, 2016, Eskisehir Osmangazi University

 İmge erişimi, bir sorgu imgesine benzeyen imgeleri birçok imge arasından bulup sıralı bir şekilde döndürme işlemidir ve yapılan birçok çalışmaya rağmen hala tam olarak çözülemeyen… (more)

Subjects/Keywords: Büyük Veri Tabanlı İmge Erişimi; Etiketleme; Transdaktif Destek Vektör Makineleri; Özetleme; Yarı Eğitmenli Öğrenme; Large Scale İmage Retrieval; Annotation; Transductive Support Vector Machines; Hashing; Semi-Supervised Learning

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APA (6th Edition):

Döngel, T. (2016). Büyük imge veri tabanlarında erişim için yarı eğitmenli görsel temsiller . (Thesis). Eskisehir Osmangazi University. Retrieved from http://hdl.handle.net/11684/1027

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

Chicago Manual of Style (16th Edition):

Döngel, Tuğçe. “Büyük imge veri tabanlarında erişim için yarı eğitmenli görsel temsiller .” 2016. Thesis, Eskisehir Osmangazi University. Accessed December 08, 2019. http://hdl.handle.net/11684/1027.

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

MLA Handbook (7th Edition):

Döngel, Tuğçe. “Büyük imge veri tabanlarında erişim için yarı eğitmenli görsel temsiller .” 2016. Web. 08 Dec 2019.

Vancouver:

Döngel T. Büyük imge veri tabanlarında erişim için yarı eğitmenli görsel temsiller . [Internet] [Thesis]. Eskisehir Osmangazi University; 2016. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/11684/1027.

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

Council of Science Editors:

Döngel T. Büyük imge veri tabanlarında erişim için yarı eğitmenli görsel temsiller . [Thesis]. Eskisehir Osmangazi University; 2016. Available from: http://hdl.handle.net/11684/1027

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

6. WANG GANG. A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video.

Degree: 2009, National University of Singapore

Subjects/Keywords: Domain Knowledge; Unlabeled Data; Text Semantics; Multi-resolution analysis; Transductive Learning; Bootstrapping.

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APA (6th Edition):

GANG, W. (2009). A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/15829

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

Chicago Manual of Style (16th Edition):

GANG, WANG. “A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video.” 2009. Thesis, National University of Singapore. Accessed December 08, 2019. http://scholarbank.nus.edu.sg/handle/10635/15829.

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

MLA Handbook (7th Edition):

GANG, WANG. “A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video.” 2009. Web. 08 Dec 2019.

Vancouver:

GANG W. A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video. [Internet] [Thesis]. National University of Singapore; 2009. [cited 2019 Dec 08]. Available from: http://scholarbank.nus.edu.sg/handle/10635/15829.

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

Council of Science Editors:

GANG W. A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video. [Thesis]. National University of Singapore; 2009. Available from: http://scholarbank.nus.edu.sg/handle/10635/15829

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

7. Grappin, Edwin. Model Averaging in Large Scale Learning : Estimateur par agrégat en apprentissage statistique en grande dimension.

Degree: Docteur es, Mathématiques fondamentales, 2018, Paris Saclay

Les travaux de cette thèse explorent les propriétés de procédures d'estimation par agrégation appliquées aux problèmes de régressions en grande dimension. Les estimateurs par agrégation… (more)

Subjects/Keywords: Apprentissage statistique; Régression; Apprentissage automatique; Estimation par agrégation; Apprentissage supervisé, semi-supervisé et transductif; PAC-Bayésien; Statistical learning; Regression; Machine learning; Estimation by aggregation; Supervized; Semi-Supervized and Transductive learning; PAC-Bayesian; 519

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APA (6th Edition):

Grappin, E. (2018). Model Averaging in Large Scale Learning : Estimateur par agrégat en apprentissage statistique en grande dimension. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2018SACLG001

Chicago Manual of Style (16th Edition):

Grappin, Edwin. “Model Averaging in Large Scale Learning : Estimateur par agrégat en apprentissage statistique en grande dimension.” 2018. Doctoral Dissertation, Paris Saclay. Accessed December 08, 2019. http://www.theses.fr/2018SACLG001.

MLA Handbook (7th Edition):

Grappin, Edwin. “Model Averaging in Large Scale Learning : Estimateur par agrégat en apprentissage statistique en grande dimension.” 2018. Web. 08 Dec 2019.

Vancouver:

Grappin E. Model Averaging in Large Scale Learning : Estimateur par agrégat en apprentissage statistique en grande dimension. [Internet] [Doctoral dissertation]. Paris Saclay; 2018. [cited 2019 Dec 08]. Available from: http://www.theses.fr/2018SACLG001.

Council of Science Editors:

Grappin E. Model Averaging in Large Scale Learning : Estimateur par agrégat en apprentissage statistique en grande dimension. [Doctoral Dissertation]. Paris Saclay; 2018. Available from: http://www.theses.fr/2018SACLG001


Pontifical Catholic University of Rio de Janeiro

8. LUIZ ALBERTO BARBOSA DE LIMA. [en] POROSITY ESTIMATION FROM SEISMIC ATTRIBUTES WITH SIMULTANEOUS CLASSIFICATION OF SPATIALLY STRUCTURED LATENT FACIES.

Degree: 2018, Pontifical Catholic University of Rio de Janeiro

[pt] Predição de porosidade em reservatórios de óleo e gás representa em uma tarefa crucial e desafiadora na indústria de petróleo. Neste trabalho é proposto… (more)

Subjects/Keywords: [pt] VARIAVEIS LATENTES; [en] LATENT VARIABLES; [pt] ESTIMATIVA DE POROSIDADE; [en] POROSITY ESTIMATION; [pt] CLASSIFICACAO DE FACIES GEOLOGICAS; [en] GEOLOGICAL FACIES CLASSIFICATION; [pt] CONDITIONAL RANDOM FIELD; [en] CONDITIONAL RANDOM FIELD; [pt] APRENDIZADO SEMI-SUPERVISIONADO; [en] SEMI-SUPERVISED LEARNING; [pt] APRENDIZADO TRANSDUTIVO; [en] TRANSDUCTIVE LEARNING

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APA (6th Edition):

LIMA, L. A. B. D. (2018). [en] POROSITY ESTIMATION FROM SEISMIC ATTRIBUTES WITH SIMULTANEOUS CLASSIFICATION OF SPATIALLY STRUCTURED LATENT FACIES. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33718

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

Chicago Manual of Style (16th Edition):

LIMA, LUIZ ALBERTO BARBOSA DE. “[en] POROSITY ESTIMATION FROM SEISMIC ATTRIBUTES WITH SIMULTANEOUS CLASSIFICATION OF SPATIALLY STRUCTURED LATENT FACIES.” 2018. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed December 08, 2019. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33718.

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

MLA Handbook (7th Edition):

LIMA, LUIZ ALBERTO BARBOSA DE. “[en] POROSITY ESTIMATION FROM SEISMIC ATTRIBUTES WITH SIMULTANEOUS CLASSIFICATION OF SPATIALLY STRUCTURED LATENT FACIES.” 2018. Web. 08 Dec 2019.

Vancouver:

LIMA LABD. [en] POROSITY ESTIMATION FROM SEISMIC ATTRIBUTES WITH SIMULTANEOUS CLASSIFICATION OF SPATIALLY STRUCTURED LATENT FACIES. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. [cited 2019 Dec 08]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33718.

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

Council of Science Editors:

LIMA LABD. [en] POROSITY ESTIMATION FROM SEISMIC ATTRIBUTES WITH SIMULTANEOUS CLASSIFICATION OF SPATIALLY STRUCTURED LATENT FACIES. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33718

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


Penn State University

9. Pal, Siddharth. MAXIMUM ENTROPY AND IMPROVED ITERATIVE SCALING FOR CLASSIFICATION ON MIXED SPACES, ENSEMBLE CLASSIFICATION AND EXTENSIONS.

Degree: PhD, Electrical Engineering, 2006, Penn State University

 Improved iterative scaling (IIS) is an algorithm for learning maximum entropy (ME) joint and conditional probability models, consistent with specified constraints, that has found great… (more)

Subjects/Keywords: Distributed Ensemble Classification; Ensemble Classification; Classification on Mixed feature spaces; Transductive learning; General Inference

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APA (6th Edition):

Pal, S. (2006). MAXIMUM ENTROPY AND IMPROVED ITERATIVE SCALING FOR CLASSIFICATION ON MIXED SPACES, ENSEMBLE CLASSIFICATION AND EXTENSIONS. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/7404

Chicago Manual of Style (16th Edition):

Pal, Siddharth. “MAXIMUM ENTROPY AND IMPROVED ITERATIVE SCALING FOR CLASSIFICATION ON MIXED SPACES, ENSEMBLE CLASSIFICATION AND EXTENSIONS.” 2006. Doctoral Dissertation, Penn State University. Accessed December 08, 2019. https://etda.libraries.psu.edu/catalog/7404.

MLA Handbook (7th Edition):

Pal, Siddharth. “MAXIMUM ENTROPY AND IMPROVED ITERATIVE SCALING FOR CLASSIFICATION ON MIXED SPACES, ENSEMBLE CLASSIFICATION AND EXTENSIONS.” 2006. Web. 08 Dec 2019.

Vancouver:

Pal S. MAXIMUM ENTROPY AND IMPROVED ITERATIVE SCALING FOR CLASSIFICATION ON MIXED SPACES, ENSEMBLE CLASSIFICATION AND EXTENSIONS. [Internet] [Doctoral dissertation]. Penn State University; 2006. [cited 2019 Dec 08]. Available from: https://etda.libraries.psu.edu/catalog/7404.

Council of Science Editors:

Pal S. MAXIMUM ENTROPY AND IMPROVED ITERATIVE SCALING FOR CLASSIFICATION ON MIXED SPACES, ENSEMBLE CLASSIFICATION AND EXTENSIONS. [Doctoral Dissertation]. Penn State University; 2006. Available from: https://etda.libraries.psu.edu/catalog/7404

10. Taewijit, Siriwon. ラベルなしデータからの医学テキストマイニングのための遠距離教師あり学習とトランスダクティブ推定.

Degree: 博士(知識科学), 2017, Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学

Supervisor:池田 満

知識科学研究科

博士

Subjects/Keywords: adverse drug reaction (ADR); medical text mining; distant supervision; multiple-instance learning (MIL); relation extraction; transductive inference

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APA (6th Edition):

Taewijit, S. (2017). ラベルなしデータからの医学テキストマイニングのための遠距離教師あり学習とトランスダクティブ推定. (Thesis). Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10119/15072

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

Chicago Manual of Style (16th Edition):

Taewijit, Siriwon. “ラベルなしデータからの医学テキストマイニングのための遠距離教師あり学習とトランスダクティブ推定.” 2017. Thesis, Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Accessed December 08, 2019. http://hdl.handle.net/10119/15072.

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

MLA Handbook (7th Edition):

Taewijit, Siriwon. “ラベルなしデータからの医学テキストマイニングのための遠距離教師あり学習とトランスダクティブ推定.” 2017. Web. 08 Dec 2019.

Vancouver:

Taewijit S. ラベルなしデータからの医学テキストマイニングのための遠距離教師あり学習とトランスダクティブ推定. [Internet] [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; 2017. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10119/15072.

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

Council of Science Editors:

Taewijit S. ラベルなしデータからの医学テキストマイニングのための遠距離教師あり学習とトランスダクティブ推定. [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; 2017. Available from: http://hdl.handle.net/10119/15072

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

11. Alzubaidi, Mohammad Abdulhadi Moh'D. Computational Methods for Perceptual Training in Radiology.

Degree: PhD, Computer Science, 2012, Arizona State University

 Medical images constitute a special class of images that are captured to allow diagnosis of disease, and their "correct" interpretation is vitally important. Because they… (more)

Subjects/Keywords: Computer science; Medical imaging and radiology; Anomaly Detection; Atypicality Detection; Chest Radiographs; Eye tracking for Radiology Training; Online Instance Selection; Semi-Transductive Learning

Transductive Learning Methods Suffer from High Computational Cost… …197 Instance Selection can be used to Reduce the Computational Cost of Transductive Learning… …A NOVEL SEMI-TRANSDUCTIVE LEARNING FRAMEWORK FOR EFFICIENT ATYPICALITY DETECTION IN CHEST… …84 The Use of Learning Machines to Generate Importance Maps… …103 An Alternative to CAD and CADe: Learning what is Normal… 

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

APA (6th Edition):

Alzubaidi, M. A. M. (2012). Computational Methods for Perceptual Training in Radiology. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/15128

Chicago Manual of Style (16th Edition):

Alzubaidi, Mohammad Abdulhadi Moh'D. “Computational Methods for Perceptual Training in Radiology.” 2012. Doctoral Dissertation, Arizona State University. Accessed December 08, 2019. http://repository.asu.edu/items/15128.

MLA Handbook (7th Edition):

Alzubaidi, Mohammad Abdulhadi Moh'D. “Computational Methods for Perceptual Training in Radiology.” 2012. Web. 08 Dec 2019.

Vancouver:

Alzubaidi MAM. Computational Methods for Perceptual Training in Radiology. [Internet] [Doctoral dissertation]. Arizona State University; 2012. [cited 2019 Dec 08]. Available from: http://repository.asu.edu/items/15128.

Council of Science Editors:

Alzubaidi MAM. Computational Methods for Perceptual Training in Radiology. [Doctoral Dissertation]. Arizona State University; 2012. Available from: http://repository.asu.edu/items/15128

.