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1. Gaussian Processes for Machine Learning: Contents

   Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN 0-262-18253-X. This book is © Copyright 2006 by Massachusetts Institute of Technology. The MIT Press have kindly agreed to allow us to make the book available on the web. The web version of the book corresponds to the 2nd printing. You can buy the book for a list price of 35.00 US$ or 22.95 UK£. List of contents and chapters in pdf format
   
   

   http://www.gaussianprocess.org/gpml/chapters/

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2. The Gaussian Processes Web Site

   The Gaussian Processes Web Site This web site aims to provide an overview of resources concerned with probabilistic modeling, inference and learning based on Gaussian processes. Although Gaussian processes have a long history in the field of statistics, they seem to have been employed extensively only in niche areas. With the advent of kernel machines in the machine learning community, models based on Gaussian processes have become commonplace for problems of regression (kriging) and classification as well as a host of more specialized applications.
   
   

   http://www.gaussianprocess.org/

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3. Bayesian network - Wikipedia, the free encyclopedia

   A Bayesian network (or a belief network ) is a probabilistic graphical model that represents a set of variables and their probabilistic independencies. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

   http://en.wikipedia.org/wiki/Bayesian_network

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4. Statistical classification - Wikipedia, the free encyclopedia

   Statistical classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, etc) and based on a training set of previously labeled items. Formally, the problem can be stated as follows: given training data produce a classifier which maps an object to its classification label . For example, if the problem is filtering spam, then is some representation of an email and y is either "Spam" or "Non-Spam".
   
   

   http://en.wikipedia.org/wiki/Statistical_classification

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5. Bayes' theorem - Wikipedia, the free encyclopedia

   Bayes' theorem (also known as Bayes' rule or Bayes' law ) is a result in probability theory that relates conditional probabilities . If A and B denote two events , P ( A | B ) denotes the conditional probability of A occurring, given that B occurs. The two conditional probabilities P ( A | B ) and P ( B | A ) are in general different. Bayes theorem gives a relation between P ( A | B ) and P ( B | A ).
   
   

   http://en.wikipedia.org/wiki/Bayes%27_theorem

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6. Data Mining, Clementine, Predictive Modeling, Predictive Analytics, Predictive Analysis

   Data Mining at SPSS. Specializing in clementines, predictive modeling, predictive analytics and predictive analysis
   
   

   http://www.spss.com/clementine/

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7. WordNet - Princeton University Cognitive Science Laboratory

   WordNet® is a large lexical database of English, developed under the direction of George A. Miller . Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser.

   http://wordnet.princeton.edu/

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