Stable Feature Selection in Empty Spaces: Applications to Gene Profiling and Diagnosis from DNA Microarrays - Softcover
Inhaltsangabe
Many technological fields produce data characterised by a steadily growing number of dimensions. That number is however often growing much faster than the number of points available. A typical illustration of this trend is Genomics. This setting makes many machine learning applications subject to the curse of dimensionality, making difficult the estimation of robust predictive models. This book focuses on the design and application of techniques achieving both sparse feature selection and estimation of models with good classification performance in high-dimensional, empty spaces. This challenge can be successfully addressed provided that adequate inductive biases are used to mitigate the lack of extra samples. Those biases can consist either of taking many different views of the data only (ensemble methods), or of the use of external extra information, either field expert prior knowledge or other datasets about related tasks (transfer learning or multi-task learning). The proposed methods are tested over gene expression microarray datasets for diagnosis and biomarker discovery. Those datasets are typically made of few tens of samples (patients) and thousands of dimensions (genes).
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Über die Autorin bzw. den Autor
Thibault Helleputte was born in Belgium in 1982. He graduated as an Engineer in Computing Science in 2006 at the Université catholique de Louvain, where he also received a PhD in 2010. A large part of his work is dedicated to engineering solutions for biomedical applications. http://www.thibaulthelleputte.be
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Stable Feature Selection in Empty Spaces
Verlag:
LAP LAMBERT Academic Publishing Mai 2011, 2011
ISBN 10: 3844390146
ISBN 13: 9783844390148
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many technological fields produce data characterised by a steadily growing number of dimensions. That number is however often growing much faster than the number of points available. A typical illustration of this trend is Genomics. This setting makes many machine learning applications subject to the curse of dimensionality, making difficult the estimation of robust predictive models. This book focuses on the design and application of techniques achieving both sparse feature selection and estimation of models with good classification performance in high-dimensional, empty spaces. This challenge can be successfully addressed provided that adequate inductive biases are used to mitigate the lack of extra samples. Those biases can consist either of taking many different views of the data only (ensemble methods), or of the use of external extra information, either field expert prior knowledge or other datasets about related tasks (transfer learning or multi-task learning). The proposed methods are tested over gene expression microarray datasets for diagnosis and biomarker discovery. Those datasets are typically made of few tens of samples (patients) and thousands of dimensions (genes). 268 pp. Englisch. Bestandsnummer des Verkäufers 9783844390148
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Stable Feature Selection in Empty Spaces
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ISBN 13: 9783844390148
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Stable Feature Selection in Empty Spaces | Applications to Gene Profiling and Diagnosis from DNA Microarrays
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844390146
ISBN 13: 9783844390148
Neu
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Taschenbuch. Zustand: Neu. Stable Feature Selection in Empty Spaces | Applications to Gene Profiling and Diagnosis from DNA Microarrays | Thibault Helleputte | Taschenbuch | 268 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844390148 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 107015528
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Stable Feature Selection in Empty Spaces
Verlag:
LAP LAMBERT Academic Publishing Mai 2011, 2011
ISBN 10: 3844390146
ISBN 13: 9783844390148
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Many technological fields produce data characterised by a steadily growing number of dimensions. That number is however often growing much faster than the number of points available. A typical illustration of this trend is Genomics. This setting makes many machine learning applications subject to the curse of dimensionality, making difficult the estimation of robust predictive models. This book focuses on the design and application of techniques achieving both sparse feature selection and estimation of models with good classification performance in high-dimensional, empty spaces. This challenge can be successfully addressed provided that adequate inductive biases are used to mitigate the lack of extra samples. Those biases can consist either of taking many different views of the data only (ensemble methods), or of the use of external extra information, either field expert prior knowledge or other datasets about related tasks (transfer learning or multi-task learning). The proposed methods are tested over gene expression microarray datasets for diagnosis and biomarker discovery. Those datasets are typically made of few tens of samples (patients) and thousands of dimensions (genes).VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 268 pp. Englisch. Bestandsnummer des Verkäufers 9783844390148
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Stable Feature Selection in Empty Spaces : Applications to Gene Profiling and Diagnosis from DNA Microarrays
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844390146
ISBN 13: 9783844390148
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many technological fields produce data characterised by a steadily growing number of dimensions. That number is however often growing much faster than the number of points available. A typical illustration of this trend is Genomics. This setting makes many machine learning applications subject to the curse of dimensionality, making difficult the estimation of robust predictive models. This book focuses on the design and application of techniques achieving both sparse feature selection and estimation of models with good classification performance in high-dimensional, empty spaces. This challenge can be successfully addressed provided that adequate inductive biases are used to mitigate the lack of extra samples. Those biases can consist either of taking many different views of the data only (ensemble methods), or of the use of external extra information, either field expert prior knowledge or other datasets about related tasks (transfer learning or multi-task learning). The proposed methods are tested over gene expression microarray datasets for diagnosis and biomarker discovery. Those datasets are typically made of few tens of samples (patients) and thousands of dimensions (genes). Bestandsnummer des Verkäufers 9783844390148
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