{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":55,"n_cols":5,"rows_kept":55,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[13,2,13,3],[14,2,14,3],[15,2,15,3],[16,2,16,3],[17,2,17,3],[18,2,18,3],[20,2,20,3],[21,2,21,3],[25,2,25,3],[33,2,33,3],[33,4,33,5],[34,2,34,3],[34,4,34,5],[35,2,35,3],[35,4,35,5],[36,2,36,3],[37,2,37,3],[40,2,40,3],[41,2,41,3],[42,2,42,3],[47,2,47,3],[48,2,48,3],[52,2,52,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910818427003321"]
[null,null,"Autore",null,"Kulkarni Sanjeev"]
[null,null,"Titolo",null,"An elementary introduction to statistical learning theory / / Sanjeev Kulkarni, Gilbert Harman"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J., : Wiley, c2011"]
[null,null,"ISBN",null,"9786613098689"]
[{"$nulls":4},"9781283098687"]
[{"$nulls":4},"1283098687"]
[{"$nulls":4},"9781118023464"]
[{"$nulls":4},"1118023463"]
[{"$nulls":4},"9781118023471"]
[{"$nulls":4},"1118023471"]
[{"$nulls":4},"9781118023433"]
[{"$nulls":4},"1118023439"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (235 p.)"]
[null,null,"Collana",null,"Wiley series in probability and statistics"]
[null,null,"Classificazione",null,"ST 300"]
[null,null,"Disciplina",null,"006.3/1"]
[null,null,"Soggetti",null,"Machine learning - Statistical methods"]
[{"$nulls":4},"Pattern recognition systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"An Elementary Introduction to Statistical Learning Theory; Contents; Preface; 1 Introduction: Classification, Learning, Features, and Applications; 1.1 Scope; 1.2 Why Machine Learning?; 1.3 Some Applications; 1.3.1 Image Recognition; 1.3.2 Speech Recognition; 1.3.3 Medical Diagnosis; 1.3.4 Statistical Arbitrage; 1.4 Measurements, Features, and Feature Vectors; 1.5 The Need for Probability; 1.6 Supervised Learning; 1.7 Summary; 1.8 Appendix: Induction; 1.9 Questions; 1.10 References; 2 Probability; 2.1 Probability of Some Basic Events; 2.2 Probabilities of Compound Events"]
[{"$nulls":4},"2.3 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 "]
[{"$nulls":4},"Laws of Large Numbers; 3.8 Summary; 3.9 Appendix: Measurability; 3.10 Questions; 3.11 References"]
[{"$nulls":4},"4 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"]
[{"$nulls":4},"6.2 Training Data6.3 Assumptions on the Training Data; 6.4 A Brute Force Approach to Learning; 6.5 Curse of Dimensionality, Inductive Bias, and No Free Lunch; 6.6 Summary; 6.7 Appendix: What Sort of Learning?; 6.8 Questions; 6.9 References; 7 The Nearest Neighbor Rule; 7.1 The Nearest Neighbor Rule; 7.2 Performance of the Nearest Neighbor Rule; 7.3 Intuition and Proof Sketch of Performance; 7.4 Using more Neighbors; 7.5 Summary; 7.6 Appendix: When People use Nearest Neighbor Reasoning; 7.6.1 Who Is a Bachelor?; 7.6.2 Legal Reasoning; 7.6.3 Moral Reasoning; 7.7 Questions; 7.8 References"]
[{"$nulls":4},"8 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"]
[{"$nulls":4},"10 Multilayer Networks"]
[null,null,null,"Sommario/riassunto","A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning  A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary ma"]
[null,"2.","Record Nr.",null,"UNINA9910219983203321"]
[null,null,"Autore",null,"Libicki Martin C"]
[null,null,"Titolo",null,"Cyberdeterrence and cyberwar / / Martin C. Libicki"]
[null,null,"Pubbl/distr/stampa",null,"Santa Monica, CA, : RAND, 2009"]
[null,null,"ISBN",null,"1-282-39853-9"]
[{"$nulls":4},"9786612398537"]
[{"$nulls":4},"0-8330-4875-9"]
[null,null,"Descrizione fisica",null,"1 online resource (239 p.)"]
[null,null,"Disciplina",null,"363.3250973"]
[null,null,"Soggetti",null,"Information warfare - United States"]
[{"$nulls":4},"Cyberterrorism - United States - Prevention"]
[{"$nulls":4},"Cyberspace - Security measures"]
[{"$nulls":4},"Computer networks - Security measures - United States"]
[{"$nulls":4},"Civil defense - United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Cover; Preface; Contents; Figures; Tables; Summary; Acknowledgements; Abbreviations; CHAPTER ONE - Introduction; CHAPTER TWO - A Conceptual Framework; CHAPTER THREE - Why Cyberdeterrence Is Different; CHAPTER FOUR - Why the Purpose of the Original CyberattackMatters; CHAPTER FIVE - A Strategy of Response; CHAPTER SIX - Strategic Cyberwar; CHAPTER SEVEN - Operational Cyberwar; CHAPTER EIGHT - Cyberdefense; CHAPTER NINE - Tricky Terrain; APPENDIX A - What Constitutes an Act of War in Cyberspace?; APPENDIX B - The Calculus of Explicit Versus Implicit Deterrence"]
[{"$nulls":4},"APPENDIX C - The Dim Prospects for Cyber Arms ControlReferences"]
[null,null,null,"Sommario/riassunto","Cyberspace, where information--and hence serious value--is stored and manipulated, is a tempting target. An attacker could be a person, group, or state and may disrupt or corrupt the systems from which cyberspace is built. When states are involved, it is tempting to compare fights to warfare, but there are important differences. The author addresses these differences and ways the United States protect itself in the face of attack."]