000			03206cam a2200325 a 4500
999			_c8699 _d8699
001			18114038
003			OSt
005			20200226113817.0
008			140412s2014 ne a b 001 0 eng
010			_a 2014003894
020			_a9780123985378 (paperback)
040			_aDLC _beng _cDLC _erda _dLNU
042			_apcc
050	0	0	_aQ325.5 _bC668 2014
082	0	0	_a006.31 _223
245	0	0	_aConformal prediction for reliable machine learning : _btheory, adaptations, and applications / _c[edited by] Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk.
264		1	_aAmsterdam ; _aBoston : _bElsevier/Morgan Kaufmann, _c[2014]
300			_axxiii, 298 p : _bill ; _c24 cm
504			_aIncludes bibliographical references (pages 273-293) and index.
505	8		_aMachine generated contents note: Section I: Theory 1: The Basic Conformal Prediction Framework 2: Beyond the Basic Conformal Prediction Framework Section II: Adaptations 3: Active Learning using Conformal Prediction 4: Anomaly Detection 5: Online Change Detection by Testing Exchangeability 6. Feature Selection and Conformal Predictors 7. Model Selection 8. Quality Assessment 9. Other Adaptations Section III: Applications 10. Biometrics 11. Diagnostics and Prognostics by Conformal Predictors 12. Biomedical Applications using Conformal Predictors 13. Reliable Network Traffic Classification and Demand Prediction 14. Other Applications.
520			_a"Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensionality of modern data sets has profound implications on the type of analysis that can be done. Recently several kernel-based machine learning algorithms have been developed for dealing with high-dimensional problems, where a large number of features could cause a combinatorial explosion. These methods are quickly gaining popularity, and it is widely believed that they will help to meet the challenge of analysing very large data sets. Learning machines often perform well in a wide range of applications and have nice theoretical properties without requiring any parametric statistical assumption about the source of data (unlike traditional statistical techniques). However, a typical drawback of many machine learning algorithms is that they usually do not provide any useful measure of con dence in the predicted labels of new, unclassi ed examples. Con dence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"-- _cProvided by publisher.
650		0	_aMachine learning. _935
650		0	_aArtificial intelligence _936
700	1		_aBalasubramanian, Vineeth, _eeditor of compilation. _937
700	1		_aHo, Shen-Shyang, _eeditor of compilation. _938
700	1		_aVovk, Vladimir, _d1960- _eeditor of compilation. _939
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK