Innovations in Machine Learning

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained.

Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.

• Case-based Machine Learning Paradigms
• Computational Algorithms in Machine Learning
• Explanation-based Learning Paradigms
• algorithm
• algorithms
• artificial intelligence
• genetic algorithms
• intelligence
• kernel
• learning
• logic programming
• machine learning
• neural networks
• programming

• Department of Statistics and Applied Probability, University of California at Santa Barbara, South Hall, Santa Barbara, USA

  Dawn E. Holmes

• School of Electrical & Information Engineering, Knowledge-Based Intelligent Engineering, Adelaide, Australia

  Lakhmi C. Jain

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