LEADER 05722nam 2200757Ia 450 001 9910139455203321 005 20210209180624.0 010 $a1-283-09868-7 010 $a9786613098689 010 $a1-118-02346-3 010 $a1-118-02347-1 010 $a1-118-02343-9 035 $a(CKB)2550000000032266 035 $a(EBL)697570 035 $a(OCoLC)729724626 035 $a(SSID)ssj0000476962 035 $a(PQKBManifestationID)11295636 035 $a(PQKBTitleCode)TC0000476962 035 $a(PQKBWorkID)10501657 035 $a(PQKB)10879663 035 $a(MiAaPQ)EBC697570 035 $a(MiAaPQ)EBC4030500 035 $a(Au-PeEL)EBL4030500 035 $a(CaPaEBR)ebr11107015 035 $a(CaONFJC)MIL309868 035 $a(OCoLC)927501377 035 $a(PPN)185056555 035 $a(EXLCZ)992550000000032266 100 $a20101123d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 13$aAn elementary introduction to statistical learning theory$b[electronic resource] /$fSanjeev Kulkarni, Gilbert Harman 205 $a1st ed. 210 $aHoboken, N.J. $cWiley$dc2011 215 $a1 online resource (235 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-64183-5 320 $aIncludes bibliographical references and index. 327 $aAn Elementary Introduction to Statistical Learning Theory; Contents; Preface; 1 Introduction: Classification, Learning, Features, and Applications; 1.1 Scope; 1.2 Why Machine Learning?; 1.3 Some Applications; 1.3.1 Image Recognition; 1.3.2 Speech Recognition; 1.3.3 Medical Diagnosis; 1.3.4 Statistical Arbitrage; 1.4 Measurements, Features, and Feature Vectors; 1.5 The Need for Probability; 1.6 Supervised Learning; 1.7 Summary; 1.8 Appendix: Induction; 1.9 Questions; 1.10 References; 2 Probability; 2.1 Probability of Some Basic Events; 2.2 Probabilities of Compound Events 327 $a2.3 Conditional Probability2.4 Drawing Without Replacement; 2.5 A Classic Birthday Problem; 2.6 Random Variables; 2.7 Expected Value; 2.8 Variance; 2.9 Summary; 2.10 Appendix: Interpretations of Probability; 2.11 Questions; 2.12 References; 3 Probability Densities; 3.1 An Example in Two Dimensions; 3.2 Random Numbers in [0,1]; 3.3 Density Functions; 3.4 Probability Densities in Higher Dimensions; 3.5 Joint and Conditional Densities; 3.6 Expected Value and Variance; 3.7 Laws of Large Numbers; 3.8 Summary; 3.9 Appendix: Measurability; 3.10 Questions; 3.11 References 327 $a4 The Pattern Recognition Problem4.1 A Simple Example; 4.2 Decision Rules; 4.3 Success Criterion; 4.4 The Best Classifier: Bayes Decision Rule; 4.5 Continuous Features and Densities; 4.6 Summary; 4.7 Appendix: Uncountably Many; 4.8 Questions; 4.9 References; 5 The Optimal Bayes Decision Rule; 5.1 Bayes Theorem; 5.2 Bayes Decision Rule; 5.3 Optimality and Some Comments; 5.4 An Example; 5.5 Bayes Theorem and Decision Rule with Densities; 5.6 Summary; 5.7 Appendix: Defining Conditional Probability; 5.8 Questions; 5.9 References; 6 Learning from Examples; 6.1 Lack of Knowledge of Distributions 327 $a6.2 Training Data6.3 Assumptions on the Training Data; 6.4 A Brute Force Approach to Learning; 6.5 Curse of Dimensionality, Inductive Bias, and No Free Lunch; 6.6 Summary; 6.7 Appendix: What Sort of Learning?; 6.8 Questions; 6.9 References; 7 The Nearest Neighbor Rule; 7.1 The Nearest Neighbor Rule; 7.2 Performance of the Nearest Neighbor Rule; 7.3 Intuition and Proof Sketch of Performance; 7.4 Using more Neighbors; 7.5 Summary; 7.6 Appendix: When People use Nearest Neighbor Reasoning; 7.6.1 Who Is a Bachelor?; 7.6.2 Legal Reasoning; 7.6.3 Moral Reasoning; 7.7 Questions; 7.8 References 327 $a8 Kernel Rules8.1 Motivation; 8.2 A Variation on Nearest Neighbor Rules; 8.3 Kernel Rules; 8.4 Universal Consistency of Kernel Rules; 8.5 Potential Functions; 8.6 More General Kernels; 8.7 Summary; 8.8 Appendix: Kernels, Similarity, and Features; 8.9 Questions; 8.10 References; 9 Neural Networks: Perceptrons; 9.1 Multilayer Feedforward Networks; 9.2 Neural Networks for Learning and Classification; 9.3 Perceptrons; 9.3.1 Threshold; 9.4 Learning Rule for Perceptrons; 9.5 Representational Capabilities of Perceptrons; 9.6 Summary; 9.7 Appendix: Models of Mind; 9.8 Questions; 9.9 References 327 $a10 Multilayer Networks 330 $aA thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary ma 410 0$aWiley series in probability and statistics. 606 $aMachine learning$xStatistical methods 606 $aPattern recognition systems 615 0$aMachine learning$xStatistical methods. 615 0$aPattern recognition systems. 676 $a006.3/1 676 $a006.31 686 $aST 300$2rvk 700 $aKulkarni$b Sanjeev$0502753 701 $aHarman$b Gilbert$0160614 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139455203321 996 $aElementary introduction to statistical learning theory$91734732 997 $aUNINA LEADER 03523oam 2200697I 450 001 9910789954803321 005 20230801221919.0 010 $a9780208805060 010 $a1-283-46244-3 010 $a9786613462442 010 $a0-203-80506-2 010 $a1-136-64547-0 024 7 $a10.4324/9780203805060 035 $a(CKB)2670000000161285 035 $a(EBL)958284 035 $a(OCoLC)798532162 035 $a(SSID)ssj0000678070 035 $a(PQKBManifestationID)11396875 035 $a(PQKBTitleCode)TC0000678070 035 $a(PQKBWorkID)10696997 035 $a(PQKB)11595570 035 $a(MiAaPQ)EBC958284 035 $a(Au-PeEL)EBL958284 035 $a(CaPaEBR)ebr10535038 035 $a(CaONFJC)MIL346244 035 $a(OCoLC)782918213 035 $a(EXLCZ)992670000000161285 100 $a20180706d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aJewish women writers in the Soviet Union /$fRina Lapidus 210 1$aAbingdon, Oxon :$cRoutledge,$d2012. 215 $a1 online resource (225 p.) 225 1 $aRoutledge studies in the history of Russia and Eastern Europe 300 $aDescription based upon print version of record. 311 $a0-415-61762-6 320 $aIncludes bibliographical references and index. 327 $aCover; Jewish Women Writers in the Soviet Union; Copyright; Contents; Acknowledgements; Introduction : Jewish women writers in the Soviet Union and the present study; 1. Literature and the political regime in Russia; 2. Alexandra Brushtein (1884-1968): The tears behind the smiles; 3. Elizaveta Polonskaia (1890-1969): A concealed storm of emotion; 4. Raisa Bloch (1899-1943): A genius unaware of her talent; 5. Hanna Levina (1900-1969): A Jewish communist fighter; 6. Ol'ga Ziv (1904-1963): An unknown Jewish author; 7. Yulia Neiman (1907-1994): Brilliant philosopher and poetess 330 $aThis book presents the lives and works of eleven Jewish women authors who lived in the Soviet Union, and who wrote and published their works in Russian. The works include poems, novels, memoirs and other writing. The book provides an overview of the life of each author, an overview of each author's literary output, and an assessment of each author's often conflicted view of her ""feminine self"" and of her ""Jewish self"". At a time when the large Jewish population which lived within the Soviet Union was threatened under Stalin's prosecutions the book provides highly-informative insi 410 0$aRoutledge studies in the history of Russia and Eastern Europe. 606 $aJewish women$zSoviet Union$xIntellectual life 606 $aJudaism and literature$zSoviet Union 606 $aRussian literature$xJewish authors$xHistory and criticism 606 $aRussian literature$xWomen authors$xHistory and criticism 606 $aRussian literature$y20th century$xHistory and criticism 615 0$aJewish women$xIntellectual life. 615 0$aJudaism and literature 615 0$aRussian literature$xJewish authors$xHistory and criticism. 615 0$aRussian literature$xWomen authors$xHistory and criticism. 615 0$aRussian literature$xHistory and criticism. 676 $a891.709/9287089924 700 $aLapidus$b Rina.$0850401 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910789954803321 996 $aJewish women writers in the Soviet Union$93865319 997 $aUNINA