000			06326cam a2200565Ia 4500
001			ocn878922864
003			OCoLC
005			20190328114807.0
006			m o d
007			cr cnu---unuuu
008			140502s2014 ne ob 001 0 eng d
040			_aIDEBK _beng _epn _cIDEBK _dN$T _dYDXCP _dOPELS _dE7B _dUIU _dCDX _dOCLCF _dTPH _dB24X7 _dCOO _dMFS _dRIV _dOCLCQ _dOCLCO _dOCLCQ _dLIV _dOCLCQ _dU3W _dD6H _dINT _dOTZ _dAU@ _dOCLCQ _dWYU _dTKN
019			_a1066018126
020			_a9780124017153 _q(electronic bk.)
020			_a0124017150 _q(electronic bk.)
020			_a1306697484 _q(electronic bk.)
020			_a9781306697484 _q(electronic bk.)
020			_z9780123985378
020			_z0123985374
035			_a(OCoLC)878922864 _z(OCoLC)1066018126
050		4	_aQ325.5 _bC668 2014eb
060		4	_aOnline Book
072		7	_aCOM _x000000 _2bisacsh
082	0	4	_a006.3/1 _223
245	0	0	_aConformal prediction for reliable machine learning : theory, adaptations, and applications / _h[electronic resource] _cedited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk.
260			_aAmsterdam ; _aBoston : _bMorgan Kaufmann, _c�2014.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
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 confidence in the predicted labels of new, unclassifed examples. Confidence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"-- _cProvided by publisher.
504			_aIncludes bibliographical references and index.
588	0		_aPrint version record.
505	0		_aHalf Title; Title Page; Copyright; Copyright Permissions; Contents; Contributing Authors; Foreword; Preface; Book Organization; Part I: Theory; Part II: Adaptations; Part III: Applications; Companion Website; Contacting Us; Acknowledgments; Part I: Theory; 1 The Basic Conformal Prediction Framework; 1.1 The Basic Setting and Assumptions; 1.2 Set and Confidence Predictors; 1.2.1 Validity and Efficiency of Set and Confidence Predictors; 1.3 Conformal Prediction; 1.3.1 The Binary Case; 1.3.2 The Gaussian Case; 1.4 Efficiency in the Case of Prediction without Objects.
505	8		_a1.5 Universality of Conformal Predictors1.6 Structured Case and Classification; 1.7 Regression; 1.8 Additional Properties of Validity and Efficiency in the Online Framework; 1.8.1 Asymptotically Efficient Conformal Predictors; Acknowledgments; 2 Beyond the Basic Conformal Prediction Framework; 2.1 Conditional Validity; 2.2 Conditional Conformal Predictors; 2.2.1 Venn's Dilemma; 2.3 Inductive Conformal Predictors; 2.3.1 Conditional Inductive Conformal Predictors; 2.4 Training Conditional Validity of Inductive Conformal Predictors; 2.5 Classical Tolerance Regions.
505	8		_a2.6 Object Conditional Validity and Efficiency2.6.1 Negative Result; 2.6.2 Positive Results; 2.7 Label Conditional Validity and ROC Curves; 2.8 Venn Predictors; 2.8.1 Inductive Venn Predictors; 2.8.2 Venn Prediction without Objects; Acknowledgments; Part II: Adaptations; 3 Active Learning; 3.1 Introduction; 3.2 Background and Related Work; 3.2.1 Pool-based Active Learning with Serial Query; SVM-based methods; Statistical methods; Ensemble-based methods; Other methods; 3.2.2 Batch Mode Active Learning; 3.2.3 Online Active Learning; 3.3 Active Learning Using Conformal Prediction.
505	8		_a3.3.1 Query by Transduction (QBT)Algorithmic formulation; 3.3.2 Generalized Query by Transduction; Algorithmic formulation; Combining multiple criteria in GQBT; 3.3.3 Multicriteria Extension to QBT; 3.4 Experimental Results; 3.4.1 Benchmark Datasets; 3.4.2 Application to Face Recognition; 3.4.3 Multicriteria Extension to QBT; 3.5 Discussion and Conclusions; Acknowledgments; 4 Anomaly Detection; 4.1 Introduction; 4.2 Background; 4.3 Conformal Prediction for Multiclass Anomaly Detection; 4.3.1 A Nonconformity Measure for Multiclass Anomaly Detection; 4.4 Conformal Anomaly Detection.
505	8		_a4.4.1 Conformal Anomalies4.4.2 Offline versus Online Conformal Anomaly Detection; 4.4.3 Unsupervised and Semi-supervised Conformal Anomaly Detection; 4.4.4 Classification Performance and Tuning of the Anomaly Threshold; 4.5 Inductive Conformal Anomaly Detection; 4.5.1 Offline and Semi-Offline Inductive Conformal Anomaly Detection; 4.5.2 Online Inductive Conformal Anomaly Detection; 4.6 Nonconformity Measures for Examples Represented as Sets of Points; 4.6.1 The Directed Hausdorff Distance; 4.6.2 The Directed Hausdorff k-Nearest Neighbors Nonconformity Measure.
650		0	_aMachine learning.
650		7	_aCOMPUTERS _xGeneral. _2bisacsh
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795
655		4	_aElectronic books.
655		4	_aLlibres electr�onics.
655		0	_aElectronic book.
700	1		_aBalasubramanian, Vineeth, _eeditor.
700	1		_aHo, Shen-Shyang, _eeditor.
700	1		_aVovk, Vladimir, _d1960- _eeditor.
776	0	8	_iPrint version: _tConformal prediction for reliable machine learning. _dAmsterdam ; Boston : Morgan Kaufmann, 2014 _z9780123985378
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780123985378
999			_c246908 _d246908