Stan Matwin & Jan Mielniczuk 
Challenges in Computational Statistics and Data Mining [PDF ebook] 

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This volume contains nineteen research papers belonging to theareas 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. Thebook’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.

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Table des matières

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.

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Langue Anglais ● Format PDF ● Pages 399 ● ISBN 9783319187815 ● Éditeur Stan Matwin & Jan Mielniczuk ● Maison d’édition Springer International Publishing ● Lieu Cham ● Pays CH ● Publié 2015 ● Téléchargeable 24 mois ● Devise EUR ● ID 4410126 ● Protection contre la copie DRM sociale

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