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Learning with support vector machines /

Campbell, Colin.

Learning with support vector machines /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.31
書名/作者:	Learning with support vector machines // Colin Campbell, Yiming Ying.
作者:	Campbell, Colin.
其他作者:	Ying, Yiming.
出版者:	[San Rafael, Calif.] : : Morgan & Claypool,, c2011.
面頁冊數:	viii, 83 p. : : ill. ;; 24 cm.
叢書名:	Synthesis lectures on artificial intelligence and machine learning,
標題:	Support vector machines.
ISBN:	9781608456161 (pbk.) :
ISBN:	1608456161 (pbk.)
ISBN:	9781608456178 (ebook)
ISBN:	160845617X (ebook)
書目註:	Includes bibliographical references (p. 75-82).
內容註:	1. Support Vector Machines for classification -- Introduction -- Support Vector Machines for binary classification -- Multi-class classification -- Learning with noise: soft margins -- Algorithmic implementation of Support Vector Machines -- Case Study 1: training a Support Vector Machine -- Case Study 2: predicting disease progression -- Case Study 3: drug discovery through active learning -- -
摘要、提要註:	Support vector machines have become a well-established tool within machine learning.They work well in practice and have now been used across a wide range of applications from recognizing handwritten digits, to face identification, text categorization, bioinformatics and database marketing. In this book we give an introductory overview of this subject. We start with a simple support vector machine for performing binary classification before considering multi-class classification and learning in the presence of noise.We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.

Learning with support vector machines /
Campbell, Colin.

Learning with support vector machines /Colin Campbell, Yiming Ying. - [San Rafael, Calif.] :Morgan & Claypool,c2011. - viii, 83 p. :ill. ;24 cm. - Synthesis lectures on artificial intelligence and machine learning,#101939-4608 ;.

Includes bibliographical references (p. 75-82).

1. Support Vector Machines for classification -- Introduction -- Support Vector Machines for binary classification -- Multi-class classification -- Learning with noise: soft margins -- Algorithmic implementation of Support Vector Machines -- Case Study 1: training a Support Vector Machine -- Case Study 2: predicting disease progression -- Case Study 3: drug discovery through active learning -- -

Support vector machines have become a well-established tool within machine learning.They work well in practice and have now been used across a wide range of applications from recognizing handwritten digits, to face identification, text categorization, bioinformatics and database marketing. In this book we give an introductory overview of this subject. We start with a simple support vector machine for performing binary classification before considering multi-class classification and learning in the presence of noise.We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.

ISBN: 9781608456161 (pbk.) :NTD 1,068Subjects--Topical Terms:

418315
Support vector machines.

Dewey Class. No.: 006.31

Learning with support vector machines /
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020 $a 9781608456161 (pbk.) : $c NTD 1,068
020 $a 1608456161 (pbk.)
020 $a 9781608456178 (ebook)
020 $a 160845617X (ebook)
040 $a NHM $c NHM $d YDXCP $d KKS $d BTCTA $d CIN $d DYU
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100 1 $a Campbell, Colin. $3 461203
245 1 0 $a Learning with support vector machines / $c Colin Campbell, Yiming Ying.
260 $a [San Rafael, Calif.] : $b Morgan & Claypool, $c c2011.
300 $a viii, 83 p. : $b ill. ; $c 24 cm.
440 0 $a Synthesis lectures on artificial intelligence and machine learning, $x 1939-4608 ; $v #10
504 $a Includes bibliographical references (p. 75-82).
505 0 $a 1. Support Vector Machines for classification -- Introduction -- Support Vector Machines for binary classification -- Multi-class classification -- Learning with noise: soft margins -- Algorithmic implementation of Support Vector Machines -- Case Study 1: training a Support Vector Machine -- Case Study 2: predicting disease progression -- Case Study 3: drug discovery through active learning -- -
505 0 $a 2. Kernel-based models -- Introduction -- Other kernel-based learning machines -- Introducing a confidence measure -- One class classification -- Regression: learning with real-valued labels -- Structured output learning -- -
505 0 $a 3. Learning with kernels -- Introduction -- Properties of kernels -- Simple kernels -- Kernels for strings and sequences -- Kernels for graphs -- Multiple kernel learning -- Learning kernel combinations via a maximum margin approach -- Algorithmic approaches to multiple kernel learning -- Case study 4: protein fold prediction -- -
505 0 $a A. Appendix -- A.1. Introduction to optimization theory -- A.2. Duality -- A.3. Constrained optimization -- Bibliography -- Authors' biography.
520 $a Support vector machines have become a well-established tool within machine learning.They work well in practice and have now been used across a wide range of applications from recognizing handwritten digits, to face identification, text categorization, bioinformatics and database marketing. In this book we give an introductory overview of this subject. We start with a simple support vector machine for performing binary classification before considering multi-class classification and learning in the presence of noise.We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.
530 $a Online version also available.
650 0 $a Support vector machines. $3 418315
700 1 $a Ying, Yiming. $3 461204