PUBLIC MARKS from ogrisel with tag tutorial

Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.

Topics: Energy models, causal generative models vs. energy models in overcomplete ICA, contrastive divergence learning, score matching, restricted Boltzmann machines, deep belief networks

I found a great set of tools for natural language processing. The Java package includes a sentence detector, a tokenizer, a parts-of-speech (POS) tagger, and a treebank parser. It took me a little while to figure out where to start so I thought I'd post my findings here. I'm no linguist and I don't have previous experience with NLP, but hopefully this will help some one get setup with OpenNLP.

This little guide covers a multi-threaded Hello World Tutorial for the Cell BE found in the Playstation 3. First we’ll step over the code using the deprecated libspe 1.2 and the new libspe 2.1 and finally look at the output we get from both examples.

Workshops and Conferences, Programmability Tools and Helpful Documentation, Papers and Collaborative Research and Demos

Online videos of talks or lectures about Machine Learning related topics

Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called Bayesian reasoning. They sound really enthusiastic about it, too, so you google and find a webpage about Bayes' Theorem and... It's this equation. That's all. Just one equation. The page you found gives a definition of it, but it doesn't say what it is, or why it's useful, or why your friends would be interested in it. It looks like this random statistics thing.