Machine Learning: A Probabilistic Perspective Kevin P. Murphy
Publisher: MIT Press

2012-12-27 10:24 414人阅读 评论(0) 收藏 举报. Buy "Machine Learning: A Probabilistic Perspective (Adaptive Computation And Machine Learning Series)" Reviews. -- Manfred Jaeger, Aalborg Universitet Keywords » Bayesian Networks - Data Mining - Density Estimation - Hybrid Random Fields - Intelligent Systems - Kernel Methods - Machine Learning - Markov Random Fields - Probabilistic Graphical Models. Nov 7, 2013 - This will follow Kevin Murphy's example in chapter 21 of Machine Learning: A Probabilistic Perspective, but we'll write the code in python with numpy and scipy. In these terms, the goal of most “machine learning” applications is to maximize (regularized/penalized) likelihood on the training corpus, or sometimes with respect to a held-out corpus if there are unmodeled parameters such as quantity of regularization. Dec 27, 2012 - A new Machine Learning Book：“Machine Learning：A Probabilistic Perspective”. Apr 12, 2013 - Generative models provide a probabilistic model of the predictors, here the words w, and the categories z, whereas discriminative models only provide a probabilistic model of the categories z given the words w. Mar 10, 2011 - The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. You can purchase the product with peace of mind here because we provide Secure Transaction. 6 days ago - Theory of Convex Optimization for Machine Learning / Estimation in high dimensions: a geometric perspective. Ľ者 Kevin P Murphy的主页：http://www.cs.ubc.ca/~murphyk/；.