Advanced Lectures on Machine Learning: Machine Learning Summer School ... : Revised LecturesSpringer, 2003 - Machine learning |
Contents
An Introduction to Pattern Classification | 1 |
Some Notes on Applied Mathematics for Machine Learning | 21 |
An Introduction to Principles and Practice | 41 |
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apply approximation assume assumption Bayesian inference Boucheron Bousquet classification algorithms clustering complex compute concentration inequalities conditional constraint convergence covariance data points data sets defined denote density dimensional directed graph Efron-Stein empirical processes empirical risk error estimate example exponential factor graph feature selection finite framework Gaussian process given graphical models Hoeffding's inequality hyperparameters input inverse iteration learning algorithm Lemma linear log₂ loss function Lugosi Machine Learning marginal likelihood Markov chain Massart Mathematics matrix measure methods minimize Monte Carlo Neural Networks nodes observed obtain optimization parameters pattern classification possible posterior distribution prior probabilistic probability distribution problem proof R(gn Rademacher averages result samples self-bounding simple Sobolev inequalities solution statistical learning theory stochastic gradient stochastic gradient descent Talagrand Theorem training data training set undirected unsupervised learning upper bound Vapnik Var(Z variance VC dimension VC entropy Xi+1