Valid Generalisation of Functions from Close Approximations on a Sample
Anthony, Martin and Shawe-Taylor, John (1994) Valid Generalisation of Functions from Close Approximations on a Sample. In, Proceedings of the First European Conference on Computational Learning Theory, EuroCOLT'93. , Oxford University Press.
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This volume contains 17 of the contributed papers presented at the 1st European Conference on Computational Learning Theory. Also included are invited presentations on the complexity of learning on neural nets, on new directions in computational learning theory, and on a neurodial model for cognitive functions. The proceedings give an overview of current work in computational learning theory, ranging from results inspired by neural network research to those arising from more classical artificial intelligence approaches. The study of machine learning within the mathematical framework of complexity theory has been a relatively recent development. The burgeoning interest in the application of machine learning to a wide variety of problems from control to financial market prediction has fired a corresponding upsurge in mathematical research.
|Item Type:||Book Section|
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science
|Date Deposited:||02 Mar 2005|
|Last Modified:||02 Mar 2012 12:58|
|Contributors:||Anthony, Martin (Author)
Shawe-Taylor, John (Author)
|Publisher:||Oxford University Press|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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