High Dimensional Clustering and Applications of Learning Methods: Non-Redundant Clustering, Principal Feature Selection and Learning Methods Applied to Image- Guided Radiotherapy - Softcover
Synopsis
This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lung tumor image-guided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all non-redundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor image-guided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple- template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle.
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About the Author
Dr. Ying Cui is a scientist at Yahoo! Inc., her research is focused on information retrieval. She studied machine learning and data mining technology at Northeastern University. She is also affiliated to Massachusetts General Hospital and Harvard Medical School for applying learning techniques to medical research.
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High Dimensional Clustering and Applications of Learning Methods
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LAP LAMBERT Academic Publishing Apr 2009, 2009
ISBN 10: 3838300807
ISBN 13: 9783838300801
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lungtumor image-guided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all non-redundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor image-guided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple-template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle. 160 pp. Englisch. Seller Inventory # 9783838300801
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High Dimensional Clustering and Applications ofLearning Methods
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lungtumor image-guided radiotherapy. In the . Seller Inventory # 5410828
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High Dimensional Clustering and Applications of Learning Methods
Published by
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ISBN 10: 3838300807
ISBN 13: 9783838300801
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lung tumor image-guided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all non-redundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor image-guided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple- template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 160 pp. Englisch. Seller Inventory # 9783838300801
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High Dimensional Clustering and Applications of Learning Methods | Non-Redundant Clustering, Principal Feature Selection and Learning Methods Applied to Image- Guided Radiotherapy
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Taschenbuch. Condition: Neu. High Dimensional Clustering and Applications of Learning Methods | Non-Redundant Clustering, Principal Feature Selection and Learning Methods Applied to Image- Guided Radiotherapy | Ying Cui | Taschenbuch | 160 S. | Englisch | 2009 | LAP LAMBERT Academic Publishing | EAN 9783838300801 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 101562626
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High Dimensional Clustering and Applications of Learning Methods : Non-Redundant Clustering, Principal Feature Selection and Learning Methods Applied to Image- Guided Radiotherapy
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ISBN 13: 9783838300801
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lungtumor image-guided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all non-redundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor image-guided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple-template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle. Seller Inventory # 9783838300801
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High Dimensional Clustering and Applications of Learning Methods: Non-Redundant Clustering, Principal Feature Selection and Learning Methods Applied to Image- Guided Radiotherapy
Published by
LAP LAMBERT Academic Publishing, 2009
ISBN 10: 3838300807
ISBN 13: 9783838300801
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