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Machine Learning
Mach Learn

ISSN (printed): 0885-6125. ISSN (electronic): 1573-0565.

Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive research results on a wide range of learning methods applied to a variety of task domains including but not limited to: Methods: Inductive learning methods; Explanation-based learning; Genetic algorithms; Analogy and case-based methods; Connectionist techniques; Automated knowledge acquisition; Learning from instruction. Task Domains: Classification and recognition; Problem solving and planning; Reasoning and inference; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and motor control. The ideal paper will make a theoretical contribution supported by a computer implementation. In addition to carefully describing the learning component it should also discuss knowledge representation and performance assumptions. The article should carefully evaluate the approach through empirical studies theoretical analysis or comparison to psychological phenomena and should discuss its relation to other work in machine learning. Variations from this prototype such as critical reviews of existing work will be considered provided they make a clear contribution to the field.

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