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Challenges in Computational Statistics and Data Mining

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Cover of 'Challenges in Computational Statistics and Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Evolutionary Computation for Real-World Problems
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    Chapter 2 Selection of Significant Features Using Monte Carlo Feature Selection
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    Chapter 3 ADX Algorithm for Supervised Classification
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    Chapter 4 Estimation of Entropy from Subword Complexity
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    Chapter 5 Exact Rate of Convergence of Kernel-Based Classification Rule
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    Chapter 6 Compound Bipolar Queries: A Step Towards an Enhanced Human Consistency and Human Friendliness
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    Chapter 7 Process Inspection by Attributes Using Predicted Data
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    Chapter 8 Székely Regularization for Uplift Modeling
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    Chapter 9 Dominance-Based Rough Set Approach to Multiple Criteria Ranking with Sorting-Specific Preference Information
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    Chapter 10 On Things Not Seen
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    Chapter 11 Network Capacity Bound for Personalized Bipartite PageRank
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    Chapter 12 Dependence Factor as a Rule Evaluation Measure
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    Chapter 13 Recent Results on Nonparametric Quantile Estimation in a Simulation Model
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    Chapter 14 Adaptive Monte Carlo Maximum Likelihood
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    Chapter 15 What Do We Choose When We Err? Model Selection and Testing for Misspecified Logistic Regression Revisited
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    Chapter 16 Semiparametric Inference in Identification of Block-Oriented Systems
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    Chapter 17 Dealing with Data Difficulty Factors While Learning from Imbalanced Data
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    Chapter 18 Personal Privacy Protection in Time of Big Data
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    Chapter 19 Data Based Modeling
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