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Machine learning : = the art and sci...

Flach, Peter A.

Machine learning : = the art and science of algorithms that make sense of data /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning :/ Peter Flach.
Reminder of title:	the art and science of algorithms that make sense of data /
Author:	Flach, Peter A.
Published:	Cambridge ;Cambridge University Press, : 2012.,
Description:	xvii, 396 p. :col. ill. ; : 25 cm.;
Subject:	Machine learning - Congresses. -
ISBN:	9781107096394 (cloth) :

Machine learning : = the art and science of algorithms that make sense of data /
Flach, Peter A.

Machine learning :the art and science of algorithms that make sense of data /Peter Flach. - Cambridge ;Cambridge University Press,2012. - xvii, 396 p. :col. ill. ;25 cm.

Includes bibliographical references (p. 367-381) and index.

1. The ingredients of machine learning -- 2. Binary classification and related tasks -- 3. Beyond binary classification -- 4. Concept learning -- 5. Tree models -- 6. Rule models -- 7. Linear models -- 8. Distance-based models -- 9. Probabilistic models -- 10. Features -- 11. Model ensembles -- 12. Machine learning experiments -- Epilogue: where to go from here.

'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.

ISBN: 9781107096394 (cloth) :NT3846

LCCN: 2012289353

Nat. Bib. No.: GBB254843bnbSubjects--Topical Terms:

574552
Machine learning
--Congresses.

LC Class. No.: Q325.5 / .F53 2012

Dewey Class. No.: 006.31

Machine learning : = the art and science of algorithms that make sense of data /
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300 $a xvii, 396 p. : $b col. ill. ; $c 25 cm.
504 $a Includes bibliographical references (p. 367-381) and index.
505 0 # $a 1. The ingredients of machine learning -- 2. Binary classification and related tasks -- 3. Beyond binary classification -- 4. Concept learning -- 5. Tree models -- 6. Rule models -- 7. Linear models -- 8. Distance-based models -- 9. Probabilistic models -- 10. Features -- 11. Model ensembles -- 12. Machine learning experiments -- Epilogue: where to go from here.
520 3 $a 'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.
650 # 0 $a Machine learning $v Congresses. $3 574552 $3 727808