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Learning with support vector machines /

Ying, Yiming.

Learning with support vector machines /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/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.;
標題:	Support vector machines. -
ISBN:	9781608456161 (pbk.) :

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.) :NT950Subjects--Topical Terms:

641425
Support vector machines.

Dewey Class. No.: 005.1

Learning with support vector machines /
LDR:02838cam a22002774a 4500 001 710578
003 OCoLC
005 20120320074350.0
008 120912s2011 caua b 000 0 eng d
020 $a 9781608456161 (pbk.) : $c NT950
020 $a 1608456161 (pbk.)
020 $a 9781608456178 (ebk.)
020 $a 160845617X (ebk.)
035 $a (OCoLC)727359224 $z (OCoLC)746837821
035 $a 727359224
040 $a NHM $c NHM $d YDXCP $d KKS $d BTCTA $d CIN $d NFU
082 0 4 $a 005.1 $2 22
100 1 $a Campbell, Colin. $3 840975
245 1 0 $a Learning with support vector machines / $c Colin Campbell, Yiming Ying.
260 # $a [San Rafael, Calif.] : $c c2011. $b Morgan & Claypool,
300 $a viii, 83 p. : $b ill. ; $c 24 cm.
490 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.
650 # 0 $a Support vector machines. $3 641425
700 1 # $a Ying, Yiming. $3 840976