Del i serien Studies in Computational Intelligence

Challenges in Computational Statistics and Data Mining

Inbunden, Engelska, 2015

1 389 kr

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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.

Produktinformation

Utgivningsdatum2015-07-21
Mått155 x 235 x 28 mm
Vikt781 g
FormatInbunden
SpråkEngelska
SerieStudies in Computational Intelligence
Antal sidor399
Upplaga15001
FörlagSpringer International Publishing AG
ISBN9783319187808

Tillhör följande kategorier

Matematisk statistik inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Databaser inom Data och IT

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.