"Machine Learning and Data Mining in Pattern Recognition" - Information and Links:

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10th International Conference, MLDM 2014, St. Petersburg, Russia, July 21-24, 2014, Proceedings

Book's cover
The cover of “Machine Learning and Data Mining in Pattern Recognition” - Open Library.

"Machine Learning and Data Mining in Pattern Recognition" is published by Springer in Aug 04, 2014 - Cham and it has 550 pages.


“Machine Learning and Data Mining in Pattern Recognition” Metadata:

  • Title: ➤  Machine Learning and Data Mining in Pattern Recognition
  • Author:
  • Number of Pages: 550
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Cham

“Machine Learning and Data Mining in Pattern Recognition” Subjects and Themes:

Edition Specifications:

  • Format: paperback

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AI-generated Review of “Machine Learning and Data Mining in Pattern Recognition”:


"Machine Learning and Data Mining in Pattern Recognition" Description:

Open Data:

Intro -- Preface -- International Conference on Machine Learning and Data Mining, MLDM 2014 -- Table of Contents -- Classification and Learning -- Efficient Error Setting for Subspace Miners -- 1 Introduction -- 2 Models, Error-Setting Methods, and Performance Measures -- 2.1 Similarity Measures -- 2.2 Existing Methods for Error Setting -- 2.3 Performance Measures -- 3 Main Hypothesis -- 4 Experimental Methodology -- 4.1 Datasets -- 4.2 Mining Algorithms -- 4.3 Experimental Design -- 5 Results -- 5.1 Heuristic Performance -- 5.2 Comparison with Alternative Methods -- 6 Discussion and Conclusions -- References -- Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier -- 1 Introduction -- 2 Theoretical Basis and Concepts -- 2.1 Notations -- 2.2 Bayesian Network and Bayesian Network Classifier -- 2.3 Multi-Dimensional Bayesian Network -- 2.4 General Multi-dimensional Bayesian Network -- 3 An Exact Algorithm to Induce GMBNC -- 3.1 Algorithm Specification -- 3.2 Soudness of IPC-BNC -- 3.3 Soudness of IPC-GMBNC -- 4 Experimental Study -- 4.1 Data Sets -- 4.2 Approaches -- 4.3 Evaluation Metrics -- 4.4 Results and Discussions -- 5 Motivation and Related Works -- 6 Conclusion -- References -- A Cost-Sensitive Based Approach for Improving Associative Classification on Imbalanced Datasets -- 1 Introduction -- 2 Background Knowledge -- 2.1 Associative Classification -- 2.2 Statistically Significant Rules -- 2.3 Cost-Sensitive Learning -- 3 SSCR: Statistically Significant Cost-Sensitive Rules for Associative Classification -- 3.1 Rules Generation Step -- 3.2 Rules Pruning Step -- 3.3 Rule Ranking -- 3.4 Classification -- 4 Experimental Results -- 5 Conclusion -- References -- Multiple Regression Method Based on Unexpandable and Irreducible Convex Combinations -- 1 Introduction

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