On Efficient Agnostic Learning of Linear Combinations of Basis Functions Wee Sun Lee Dept. of Systems Engineering, RSISE, Aust. National University, Canberra, ACT 0200, Australia.

First Page		Document Content
Date: 2005-03-22 04:23:34 Probably approximately correct learning Inner product space Polynomial Function Computational complexity theory Machine learning Supervised learning Empirical risk minimization Mathematics Mathematical analysis Computational learning theory		On Efficient Agnostic Learning of Linear Combinations of Basis Functions Wee Sun Lee Dept. of Systems Engineering, RSISE, Aust. National University, Canberra, ACT 0200, Australia. Add to Reading List Source URL: users.cecs.anu.edu.au Download Document from Source Website File Size: 145,86 KB Share Document on Facebook

	Quantifying Generalization in Linearly Weighted Neural Networks (Short title: Quantifying Generalization) Martin Anthony1 and Sean B. Holden2 Abstract DocID: 1rhpW - View Document
	Cross-validation for binary classification by real-valued functions: theoretical analysis Martin Anthony Department of Mathematics London School of Economics Houghton Street DocID: 1qUs4 - View Document
	A Sufficient Condition for Polynomial Distribution-Dependent Learnability Martin Anthony Department of Mathematics London School of Economics Houghton Street DocID: 1qDTA - View Document
	Probabilistic ‘Generalization’ of Functions and Dimension-based Uniform Convergence Results Martin Anthony Department of Mathematics London School of Economics and Political Science DocID: 1ovSQ - View Document
	PAC Learning and Artificial Neural Networks Martin Anthony and Norman Biggs Department of Mathematics, London School of Economics and Political Science (University of London), Houghton St., London WC2A 2AE, United Kingdo DocID: 1on4X - View Document