Challenges in Computational Statistics and Data Mining by Stan MatwinChallenges in Computational Statistics and Data Mining by Stan Matwin

Challenges in Computational Statistics and Data Mining

byStan MatwinEditorJan Mielniczuk

Hardcover | July 21, 2015

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This volume contains nineteen research papers belonging to the

areas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors' contributions to honour and celebrate Professor Jacek Koronacki on the occcasion of his 70th birthday. The

book's related and often interconnected topics, represent Jacek Koronacki's research interests and their evolution. They also clearly indicate how close the areas of computational statistics and data mining are.

Title:Challenges in Computational Statistics and Data MiningFormat:HardcoverDimensions:399 pagesPublished:July 21, 2015Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319187805

ISBN - 13:9783319187808

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Table of Contents

Evolutionary Computation for Real-world Problems.- Selection of Significant Features Using Monte Carlo Feature Selection.- ADX Algorithm for Supervised Classification.- Estimation of Entropy from Subword Complexity.- Exact Rates of Convergence of Kernel-based Classification Rule.- Compound Bipolar Queries: a Step Towards an Enhanced Human Consistency and Human Friendliness.- Process Inspection by Attributes Using Predicted Data.- Székely Regularization for Uplift Modeling.- Dominance-Based Rough Set Approach to Multiple Criterion Ranking with Sorting-specific Preference Information.- On things not Seen.- Network Capacity Bound for Personalized Bipartite Page Rank.- Dependence Factor as a Rule Evaluation Measure.- Recent Results on Quantlie Estimation Methods in Simulation Model.- Adaptive Monte Carlo Maximum Likelihood.- What Do we Choose when we Err? Model Selection and Testing for Misspecified Logistic Regression Revisited.- Semiparametric Inference Identification of Block-oriented Systems.- Dealing with Data Difficulty Factors While Learning from Imbalanced Data.- Privacy Protection in a Time of Big Data.- Data Based Modeling.