Titlebook: Advanced Lectures on Machine Learning; ML Summer Schools 20 Olivier Bousquet,Ulrike Luxburg,Gunnar Rätsch Textbook 2004 Springer-Verlag Ber

彻底明白

假设

verdict

sleep-spindles

Big Data, Simulations and HPC ConvergenceThe goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.

Petechiae

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/145819.jpg

偶像

Big Data Application Architecture,er distinct labeling of the data (supervised learning), division of the data into classes (unsupervised learning), selection of the most significant features of the data (feature selection), or a combination of more than one of these tasks.

SMART

averse

Fecundity

Accelerating BigBench on Hadoopated from information theoretic and Bayesian principles. We briefly review basic models in unsupervised learning, including factor analysis, PCA, mixtures of Gaussians, ICA, hidden Markov models, state-space models, and many variants and extensions. We derive the EM algorithm and give an overview of

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