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

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

12th International Conference, MLDM 2016, New York, NY, USA, July 16-21, 2016, 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 Jun 28, 2016 - Cham and it has 820 pages.


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

  • Title: ➤  Machine Learning and Data Mining in Pattern Recognition
  • Author:
  • Number of Pages: 820
  • 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 -- Evolving a Low Price Recovery Strategy for Distressed Securities -- 1 Introduction -- 1.1 Problem Description -- 2 Approach -- 2.1 LPRS Agent Fitness Functions -- 2.2 Types of LPRS Agents -- 2.3 Genetic Program Characteristics -- 3 Related Work -- 4 Experimentation and Analysis of Results -- 4.1 Experiment Setup -- 4.2 Experiment Results -- 4.3 Analysis of Results -- 5 Summary -- References -- A Learning Framework to Improve Unsupervised Gene Network Inference -- 1 Introduction -- 2 The Learning Framework -- 2.1 Graph Sparsification -- 2.2 Feature Selection -- 2.3 The Link Prediction Algorithm -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Experimental Methodology -- 3.3 Experimental Results -- 4 Conclusion -- References -- A Closed Frequent Subgraph Mining Algorithm in Unique Edge Label Graphs -- 1 Introduction -- 2 Preliminary -- 2.1 Notations -- 2.2 Closure Operator and Set System -- 3 Closed Frequent Subgraph Mining in Unique Edge Label Graphs -- 3.1 Connected Subgraph System -- 3.2 Edges and Converse Edges (ECE) Representation -- 3.3 Support Closure Operation -- 3.4 The ECE-CloseSG Algorithm -- 4 Experimental Study -- 5 Related Works -- 6 Conclusion -- References -- Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths -- 1 Introduction -- 2 Proposed Method -- 3 Experimental Results -- 3.1 Synthetic Data -- 3.2 Real Data -- 4 Discussion -- 5 Related Work -- 6 Conclusion -- References -- Using Support Vector Machines for Intelligent Service Agents Decision Making -- 1 Introduction -- 2 Related Work -- 3 The Proposed Model -- 3.1 Preliminaries -- Web Services. -- Communities of Web Service. -- 3.2 Feature Engineering -- 3.3 Join Consequences -- 3.4 The Learning Model -- 4 Experimental Case Study -- 4.1 Dataset -- 4.2 Experimental Protocol -- 4.3 Results

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