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

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

14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part II

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 08, 2018 - Cham and it has 500 pages.


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

  • Title: ➤  Machine Learning and Data Mining in Pattern Recognition
  • Author:
  • Number of Pages: 500
  • 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 -- Part II -- Contents - Part I -- Fusing Dimension Reduction and Classification for Mining Interesting Frequent Patterns in Patients Data -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Case Study Context -- 3.2 Dimension Reduction -- 3.3 Classification Algorithms -- 3.4 Frequent Pattern Mining -- 3.5 The Applied Method Process Flow -- 3.6 Data -- 4 Experimental Setup and Evaluation -- 4.1 Experimental -- 4.2 Feature Selection with Dimension Reduction -- 4.3 Classification -- 4.4 Classification Results -- 4.5 Performance Evaluation -- 5 Discussion -- 6 Conclusion and Future Works -- Acknowledgments -- References -- Memory Efficient Frequent Itemset Mining -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Graph Construction -- 3.2 Edge Labeling -- 3.3 Identifying Transactions in the Graph -- 4 Performance Results -- 5 Conclusion -- References -- Fuzzy Networks Model, a Reliable Adoption in Corporations -- Abstract -- 1 Introduction -- 2 Fundamentals -- 2.1 Fuzzy Logic -- 2.2 Corporate Concepts -- 2.2.1 BSC Control Structure -- 2.2.2 Model Reference Adaptive Control Structure -- 3 Related Work -- 4 Model for Building Networks of Fuzzy Systems -- 4.1 Process Workflow to Design and Simulate Fuzzy Networks -- 4.2 Software Components Model -- 4.3 Integration to JFuzzyLogic -- 4.4 Principles of NFS -- 4.5 The Prototype -- 5 Corporate Model Application -- 5.1 Corporate Model -- 5.2 The What If? Environment -- 5.3 Utility Company Application -- 6 Conclusion -- 7 Future Work -- References -- Detection of Computer-Generated Papers Using One-Class SVM and Cluster Approaches -- Abstract -- 1 Introduction -- 2 Preliminary -- 2.1 Dynamic Dissimilarity Between Texts' Styles -- 2.2 One-Class SVM -- 3 Approach -- 3.1 One-Class SVM Classifier -- 3.2 Clustering Based Classifier

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