LEADER 04749nam 2200637Ia 450 001 9910778600003321 005 20230120010427.0 010 $a1-282-29019-3 010 $a9786612290190 010 $a0-08-095559-2 035 $a(CKB)1000000000789798 035 $a(EBL)452989 035 $a(OCoLC)316566894 035 $a(SSID)ssj0000427989 035 $a(PQKBManifestationID)11284057 035 $a(PQKBTitleCode)TC0000427989 035 $a(PQKBWorkID)10432899 035 $a(PQKB)11770268 035 $a(MiAaPQ)EBC452989 035 $a(EXLCZ)991000000000789798 100 $a20090320d1968 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSequential methods in pattern recognition and machine learning$b[electronic resource] /$fK.S. Fu 210 $aNew York $cAcademic Press$d1968 215 $a1 online resource (245 p.) 225 1 $aMathematics in science and engineering ;$vv. 52 300 $aDescription based upon print version of record. 311 $a0-12-269550-X 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Sequential Methods in Pattern Recognition and Machine Learning; Copyright Page; Contents; Preface; Chapter 1. Introduction; 1.1 Pattern Recognition; 1.2 Deterministic Classification Techniques; 1.3 Training in Linear Classifiers; 1.4 Statistical Classification Techniques; 1.5 Sequential Decision Model for Pattern Classification; 1.6 Learning in Sequential Pattern Recognition Systems; 1.7 Summary and Further Remarks; References; Chapter 2. Feature Selection and Feature Ordering; 2.1 Feature Selection and Ordering-Information Theoretic Approach 327 $a2.2 Feature Selection and Ordering-Karhunen-Loe?ve Expansion2.3 Illustrative Examples; 2.4 Summary and Further Remarks; References; Chapter 3. Forward Procedure for Finite Sequential Classification Using Modified Sequential Probability Ratio Test; 3.1 Introduction; 3.2 Modified Sequential Probability Ratio Test-Discrete Case; 3.3 Modified Sequential Probability Ratio Test-Continuous Case; 3.4 Procedure of Modified Generalized Sequential Probability Ratio Test; 3.5 Experiments in Pattern Classification; 3.6 Summary and Further Remarks; References 327 $aChapter 4. Backward Procedure for Finite Sequential Recognition Using Dynamic Programming4.1 Introduction; 4.2 Mathematical Formulation and Basic Functional Equation; 4.3 Reduction of Dimensionality; 4.4 Experiments in Pattern Classification; 4.5 Backward Procedure for Both Feature Ordering and Pattern Classification; 4.6 Experiments in Feature Ordering and Pattern Classification; 4.7 Use of Dynamic Programming for Feature-Subset Selection; 4.8 Suboptimal Sequential Pattern Recognition; 4.9 Summary and Further Remarks; References 327 $aChapter 5. Nonparametric Procedure in Sequential Pattern Classification5.1 Introduction; 5.2 Sequential Ranks and Sequential Ranking Procedure; 5.3 A Sequential Two-Sample Test Problem; 5.4 Nonparametric Design of Sequential Pattern Classifiers; 5.5 Analysis of Optimal Performance and a Multiclass Generalization; 5.6 Experimental Results and Discussions; 5.7 Summary and Further Remarks; References; Chapter 6. Bayesian Learning in Sequential Pattern Recognition Systems; 6.1 Supervised Learning Using Bayesian Estimation Techniques; 6.2 Nonsupervised Learning Using Bayesian Estimation Techniques 327 $a6.3 Bayesian Learning of Slowly Varying Patterns6.4 Learning of Parameters Using an Empirical Bayes Approach; 6.5 A General Model for Bayesian Learning Systems; 6.6 Summary and Further Remarks; References; Chapter 7. Learning in Sequential Recognition Systems Using Stochastic Approximation; 7.1 Supervised Learning Using Stochastic Approximation; 7.2 Nonsupervised Learning Using Stochastic Approximation; 7.3 A General Formulation of Nonsupervised Learning Systems Using Stochastic Approximation; 7.4 Learning of Slowly Time-Varying Parameters Using Dynamic Stochastic Approximation 327 $a7.5 Summary and Further Remarks 330 $aSequential methods in pattern recognition and machine learning 410 0$aMathematics in science and engineering ;$vv. 52. 606 $aPerceptrons 606 $aStatistical decision 606 $aMachine learning 615 0$aPerceptrons. 615 0$aStatistical decision. 615 0$aMachine learning. 676 $a001.5/3 700 $aFu$b K. 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