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Machine Learning: An Artificial Intelligence Approach Ed by R. S. Michalski, J. G. Carbonell, T. M. Mitchell

Contributor(s): Michalski, R. S [Editor] | Carbonell, J. G [Editor] | Mitchell, T. M [Editor].
Material type: materialTypeLabelBookPublisher: USA Springer-Verlag 2014Description: 572p.ISBN: 9783662124079.Subject(s): Computer EngineeringDDC classification: 006.31
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Part – 1: General issues in machine learning
An overview of machine learning
Why should machines learn?
Part – 2: Learning from examples
A comparative review of selected methods for learning from examples
A theory and methodology of inductive learning
Part – 3: Learning in problem-solving and planning
Learning by analogy: formulating and generalizing plans from past experience
Learning by experimentation: acquiring and refining problem-solving heuristics
Acquisition of proof skills in geometry
Using proofs and refutations to learn from experience
Part – 4: Learning from observation and discovery
The role of heuristics in learning by discovery: three case studies
Rediscovering chemistry with the bacon system
Learning from observation: conceptual clustering
Part – 5: Learning from instruction
Machine transformation of advice into a heuristic search procedure
Learning by being told: acquiring knowledge for information management
The instructible production system: a retrospective analysis
Part – 6: Applied learning systems
Learning efficient classification procedures and their application to chess end games
Inferring student models for intelligent computer-aided instruction
Comprehensive bibliography of machine learning

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