03020nam 2200613Ia 450 991079235350332120230725023304.00-470-62750-61-282-55102-797866125510240-470-62749-2(CKB)2670000000014475(EBL)510208(OCoLC)639165815(SSID)ssj0000365717(PQKBManifestationID)11253296(PQKBTitleCode)TC0000365717(PQKBWorkID)10414694(PQKB)10246594(MiAaPQ)EBC510208(Au-PeEL)EBL510208(CaPaEBR)ebr10375620(CaONFJC)MIL255102(EXLCZ)99267000000001447520091209d2010 uy 0engur|n|---|||||txtccrSelling America short[electronic resource] the SEC and market contrarians in the age of absurdity /Richard C. SauerHoboken, N.J. John Wiley & Sonsc20101 online resource (339 p.)Includes index.0-470-58211-1 Selling America Short: The SEC and Market Contrarians in the Age of Absurdity; Contents; Prologue; Chapter 1: Rude Awakenings; Chapter 2: The SEC Steps Out; Chapter 3: Short People; Chapter 4: Belgian Waffles; Chapter 5: AremisSoft and the Deemster from Hell; Chapter 6: Taking Out the Eurotrash; Chapter 7: In the Shadow of Enron; Chapter 8: The Easter Bunny Cometh; Chapter 9: Mired in Muck; Chapter 10: Our Tax Dollars at Work; Chapter 11: Warming the Bench; Chapter 12: The Overstock Flame Wars; Chapter 13: The Bird in the BushChapter 14: The Collapse of the American Financial Sector in One Easy LessonChapter 15: Ashes, Ashes, All Fall Down; Epiloque: Picking up the Pieces; About the Author; Index An industry insider reveals the inner workings of our financial system and the agencies who attempt to control it During his dozen years as an SEC attorney, author Richard Sauer opened and supervised some of its most notable financial cases-investigations that took him to a dozen countries and returned hundreds of millions of dollars to American investors. While a partner at a major law firm and, later, a hedge fund manager, he saw firsthand the follies and failures of our system. Now, in Selling America Short, he shares his extraordinary experiences with you. Selling AmerStock exchangesUnited StatesFinanceCorrupt practicesUnited StatesFraud investigationUnited StatesStock exchangesFinanceCorrupt practicesFraud investigation332.6092BSauer Richard1524945MiAaPQMiAaPQMiAaPQBOOK9910792353503321Selling America short3766038UNINA05467nam 2200673Ia 450 991078499600332120230721031020.01-281-91179-89786611911799981-277-172-7(CKB)1000000000407388(EBL)1679477(OCoLC)886107495(SSID)ssj0000293430(PQKBManifestationID)11234343(PQKBTitleCode)TC0000293430(PQKBWorkID)10273347(PQKB)10546193(MiAaPQ)EBC1679477(WSP)00006604(Au-PeEL)EBL1679477(CaPaEBR)ebr10255816(CaONFJC)MIL191179(EXLCZ)99100000000040738820071223d2008 uy 0engur|n|---|||||txtccrData mining with decision trees[electronic resource] /theory and applications /Lior Rokach, Oded MaimonSingapore World Scientificc20081 online resource (263 p.)Series in machine perception and artificial intelligence ;v. 69Description based upon print version of record.981-277-171-9 Includes bibliographical references (p. 215-242) and index.Preface; Contents; 1. Introduction to Decision Trees; 1.1 Data Mining and Knowledge Discovery; 1.2 Taxonomy of Data Mining Methods; 1.3 Supervised Methods; 1.3.1 Overview; 1.4 Classification Trees; 1.5 Characteristics of Classification Trees; 1.5.1 Tree Size; 1.5.2 The hierarchical nature of decision trees; 1.6 Relation to Rule Induction; 2. Growing Decision Trees; 2.0.1 Training Set; 2.0.2 Definition of the Classification Problem; 2.0.3 Induction Algorithms; 2.0.4 Probability Estimation in Decision Trees; 2.0.4.1 Laplace Correction; 2.0.4.2 No Match2.1 Algorithmic Framework for Decision Trees2.2 Stopping Criteria; 3. Evaluation of Classification Trees; 3.1 Overview; 3.2 Generalization Error; 3.2.1 Theoretical Estimation of Generalization Error; 3.2.2 Empirical Estimation of Generalization Error; 3.2.3 Alternatives to the Accuracy Measure; 3.2.4 The F-Measure; 3.2.5 Confusion Matrix; 3.2.6 Classifier Evaluation under Limited Resources; 3.2.6.1 ROC Curves; 3.2.6.2 Hit Rate Curve; 3.2.6.3 Qrecall (Quota Recall); 3.2.6.4 Lift Curve; 3.2.6.5 Pearson Correlation Coegfficient; 3.2.6.6 Area Under Curve (AUC); 3.2.6.7 Average Hit Rate3.2.6.8 Average Qrecall3.2.6.9 Potential Extract Measure (PEM); 3.2.7 Which Decision Tree Classifier is Better?; 3.2.7.1 McNemar's Test; 3.2.7.2 A Test for the Difference of Two Proportions; 3.2.7.3 The Resampled Paired t Test; 3.2.7.4 The k-fold Cross-validated Paired t Test; 3.3 Computational Complexity; 3.4 Comprehensibility; 3.5 Scalability to Large Datasets; 3.6 Robustness; 3.7 Stability; 3.8 Interestingness Measures; 3.9 Overfitting and Underfitting; 3.10 "No Free Lunch" Theorem; 4. Splitting Criteria; 4.1 Univariate Splitting Criteria; 4.1.1 Overview; 4.1.2 Impurity based Criteria4.1.3 Information Gain4.1.4 Gini Index; 4.1.5 Likelihood Ratio Chi-squared Statistics; 4.1.6 DKM Criterion; 4.1.7 Normalized Impurity-based Criteria; 4.1.8 Gain Ratio; 4.1.9 Distance Measure; 4.1.10 Binary Criteria; 4.1.11 Twoing Criterion; 4.1.12 Orthogonal Criterion; 4.1.13 Kolmogorov-Smirnov Criterion; 4.1.14 AUC Splitting Criteria; 4.1.15 Other Univariate Splitting Criteria; 4.1.16 Comparison of Univariate Splitting Criteria; 4.2 Handling Missing Values; 5. Pruning Trees; 5.1 Stopping Criteria; 5.2 Heuristic Pruning; 5.2.1 Overview; 5.2.2 Cost Complexity Pruning5.2.3 Reduced Error Pruning5.2.4 Minimum Error Pruning (MEP); 5.2.5 Pessimistic Pruning; 5.2.6 Error-Based Pruning (EBP); 5.2.7 Minimum Description Length (MDL) Pruning; 5.2.8 Other Pruning Methods; 5.2.9 Comparison of Pruning Methods; 5.3 Optimal Pruning; 6. Advanced Decision Trees; 6.1 Survey of Common Algorithms for Decision Tree Induction; 6.1.1 ID3; 6.1.2 C4.5; 6.1.3 CART; 6.1.4 CHAID; 6.1.5 QUEST.; 6.1.6 Reference to Other Algorithms; 6.1.7 Advantages and Disadvantages of Decision Trees; 6.1.8 Oblivious Decision Trees; 6.1.9 Decision Trees Inducers for Large Datasets6.1.10 Online Adaptive Decision TreesThis is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique. Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process moreSeries in machine perception and artificial intelligence ;v. 69.Data miningDecision treesData mining.Decision trees.006.312Rokach Lior620362Maimon Oded Z544247MiAaPQMiAaPQMiAaPQBOOK9910784996003321Data mining with decision trees1408483UNINA