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

Machine Learning and Data Mining in Pattern Recognition - Info and Reading Options

11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, 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 Jul 13, 2015 - Cham and it has 463 pages.


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

  • Title: ➤  Machine Learning and Data Mining in Pattern Recognition
  • Author:
  • Number of Pages: 463
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Cham

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

Edition Specifications:

  • Format: paperback

Edition Identifiers:

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 -- Organization -- Contents -- Graph Mining -- Greedy Graph Edit Distance -- 1 Introduction -- 2 Bipartite Graph Edit Distance (BP-GED) -- 2.1 Graph Edit Distance -- 2.2 Approximation of Graph Edit Distance -- 3 Greedy Graph Edit Distance (Greedy-GED) -- 4 Experimental Evaluation -- 5 Conclusions and Future Work -- References -- Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation -- 1 Introduction -- 2 Graph Edit Distance Computation -- 2.1 Graph Edit Distance -- 2.2 Bipartite Graph Edit Distance Approximation -- 2.3 Beam Search Graph Edit Distance Approximation -- 3 Sorted BP-Beam -- 3.1 Sorting Strategies -- 4 Experimental Evaluation -- 5 Conclusions and Future Work -- References -- Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning -- 1 Introduction -- 2 Methodology -- 2.1 Epileptic EEG Data Set -- 2.2 Seizure Suppression -- 2.3 Construction of EEG Synchronization Graphs -- 2.4 Feature Extraction from EEG Synchronization Graphs -- 2.5 Determining the Significance of Features -- 2.6 Autoregressive Modeling on Feature Data -- 2.7 Transfer Learning and Transformation Learning on Autoregressive Model -- 2.8 Declaration of Imminent Seizure -- 3 Results -- 3.1 Quadratic Programming Feature Selection Results -- 3.2 Baseline SVM Results -- 3.3 Autoregression vs Transfer and Transformation Learning -- 4 Conclusions and Future Work -- References -- Classification and Regression -- Local and Global Genetic Fuzzy Pattern Classifiers -- 1 Introduction -- 2 Preliminaries -- 2.1 Classification Setting -- 2.2 Fuzzy Pattern Classifiers -- 2.3 Genetic Algorithm Optimization -- 3 Genetic Algorithm for Fuzzy Pattern Classifiers -- 3.1 FPC-GA Classifier -- 3.2 FPC-LGA Classifier -- 4 Experimental Analysis -- 4.1 Datasets -- 4.2 Method

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