پنجشنبه 28 بهمن 1395
نویسنده: Frances Sanchez
Machine Learning: A Probabilistic Perspective Kevin P. Murphy
Publisher: MIT Press
Probability can be very counter-intuitive. Oct 21, 2013 - The chapter (Chap. But the most interesting differences Machine learning terms definitely sound pretty cool. Nov 12, 2012 - Algorithms for decompositions of matrices are of central importance in machine learning, signal processing and information retrieval, with SVD and NMF (Nonnegative Matrix Factorisation) being the most widely used examples. Dec 3, 2008 - For example, in statistical machine translation, alignment models are described with probability theory and fit to data, but their structure is complex enough that optimal inference is intractable, and how you do approximate inference (EM, Viterbi, beam search, etc.) is a very major issue. Dec 26, 2010 - In the previous list, I thought it would be good to recommend some lighter texts as introductions to topics like probability theory and machine learning. I'm also adding a reference for looking at probability from the Bayesian perspective. Feb 19, 2014 - In recent years, probabilistic-based machine learning methods have been developed and successfully used in many areas in bioinformatics. Probabilistic interpretations of matrix We will discuss a subset of these models from a statistical modelling perspective, building upon probabilistic generative models and generalised linear models (McCulloch and Nelder). Straight into the deep end is the way to to choose from the probability list, in order to build a base in probability theory. Based upon subsequent discussions and feedback, I've changed my view. Apr 27, 2014 - 機械学習本をいろいろと調べていたら、Kevin P. Jan 1, 2013 - 2 - Machine Learning: a Probabilistic Perspective. Different methods tackle the problem from different perspectives. 3) on Bayesian updating or learning (a most appropriate term) for discrete data is well-done in 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. Maybe the perspective of computational intelligence lends itself to cool names. Compared to Bishop's Machine Learning book, this one is much easier to follow! 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.