Lakhmi C Jain, Sheela Ramanna, Robert J. Howlett




Emerging Paradigms in Machine Learning


State of the art of emerging paradigms in machine learning including some real world applicationsLatest research in machine learning and biologically-based techniques for the design and implementation of intelligent systemsWritten by leading experts in the field


Springer Berlin Heidelberg

BOOK (manifestation)

  • Book: 978-3-642-28699-5 (eBook)
  • Book: 978-3-642-28698-8 (Book)

  • Related objects


  • University Of South Australia
  • University Of Winnipeg
  • Kes International


  • BookChapter: Optimised Information Abstraction in Granular Min/Max Clustering
  • BookChapter: Rough Non-deterministic Information Analysis: Foundations and Its Perspective in Machine Learning
  • BookChapter: Identifying Calendar-Based Periodic Patterns
  • BookChapter: Introduction to Perception Based Computing
  • BookChapter: Rough Set and Artificial Neural Network Approach to Computational Stylistics
  • BookChapter: Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation
  • BookChapter: Emerging Trends in Machine Learning: Classification of Stochastically Episodic Events
  • BookChapter: Evolving Intelligent Systems: Methods, Algorithms and Applications
  • BookChapter: Mining Incomplete Data—A Rough Set Approach
  • BookChapter: Application of Learning Algorithms to Image Spam Evolution
  • BookChapter: Emerging Paradigms in Machine Learning: An Introduction
  • BookChapter: Workload Modeling for Multimedia Surveillance Systems
  • BookChapter: Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data
  • BookChapter: A Granular Computing Paradigm for Concept Learning
  • BookChapter: Support Vector Machines in Biomedical and Biometrical Applications
  • BookChapter: Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization
  • BookChapter: The Mamdani Expert-System with Parametric Families of Fuzzy Constraints in Evaluation of Cancer Patient Survival Length
  • BookChapter: Learning of Defaults by Agents in a Distributed Multi-Agent System Environment


  • Artificial Intelligence (Incl. Robotics)
  • Engineering
  • Computational Intelligence

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    23 TRIPLES      17 PREDICATES      24 URIs      12 LITERALS

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    21 sg:title Emerging Paradigms in Machine Learning
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