000			02689nam a22003618i 4500
001			CR9780511921803
003			UkCbUP
005			20200124160329.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			100927s2011||||enk o ||1 0|eng|d
020			_a9780511921803 (ebook)
020			_z9780521196000 (hardback)
020			_z9781107653115 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ325.5 _b.J37 2011
082	0	0	_a006.3/1 _222
100	1		_aJapkowicz, Nathalie, _eauthor.
245	1	0	_aEvaluating Learning Algorithms : _ba classification perspective / _cNathalie Japkowicz, Mohak Shah.
264		1	_aCambridge : _bCambridge University Press, _c2011.
300			_a1 online resource (xvi, 406 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_a1. Introduction -- 2. Machine Learning and Statistics Overview -- 3. Performance Measures I -- 4. Performance Measures II -- 5. Error Estimation -- 6. Statistical Significance testing --7. Datasets and Experimental Framework --8. Recent Developments -- 9. Conclusion -- Appendix A: Statistical Tables -- Appendix B: Additional Information on the Data -- Appendix C: Two Case Studies.
520			_aThe field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
650		0	_aMachine learning.
650		0	_aComputer algorithms _xEvaluation.
700	1		_aShah, Mohak, _eauthor.
776	0	8	_iPrint version: _z9780521196000
856	4	0	_uhttps://doi.org/10.1017/CBO9780511921803
999			_c522437 _d522435