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«Top»  : «Computers»  : «Artificial Intelligence»  : Machine Learning Sites
Andrew Schein's Web Page «
http://www.cis.upenn.edu/~ais
Site Details: http://www.cis.upenn.edu/~ais
  Machine learning approaches to data mining focussing on text mining applications
Computational Learning Theory «
http://www.learningtheory.org/
Site Details: http://www.learningtheory.org/
  A research field devoted to studying the design and analysis of algorithms for making predictions about the future based on past experiences. the emphasis in colt is on rigorous mathematical analysis. colt is largely concerned with computational and data
David W. Aha: Machine Learning Page «
http://home.earthlink.net/~dwaha/research/machine-learning.html
Site Details: http://home.earthlink.net/~dwaha/research/machine-learning.html
  Comprehensive machine learning resources from applications to tutorials.
Gowachin «
http://www.irisa.fr/Gowachin/
Site Details: http://www.irisa.fr/Gowachin/
  A competition on grammatical inference.
Grammatical Inference «
http://www.cs.iastate.edu/~honavar/gi/gi.html
Site Details: http://www.cs.iastate.edu/~honavar/gi/gi.html
  Repository of information on grammatical inference , automata induction , and language acquisition.
ILPnet2 «
http://www.cs.bris.ac.uk/~ILPnet2/
Site Details: http://www.cs.bris.ac.uk/~ILPnet2/
  Network of excellence in inductive logic programming.
Integrated Optimization - Artificial Intelligence «
http://www.miislita.com
Site Details: http://www.miislita.com
  Site dedicated to research of artificial intelligence algorithms applied to information retrieval , data mining and optimization methods. includes faqs and ai resources for math/science teachers and students.
k-means clustering tutorial «
http://people.revoledu.com/kardi/tutorial/kMean/index.html
Site Details: http://people.revoledu.com/kardi/tutorial/kMean/index.html
  Introduction to k-means clustering , a popular data mining and unsupervised learning algorithm. free code , software , resources and examples are available for download.
Kernel machines «
http://www.kernel-machines.org
Site Details: http://www.kernel-machines.org
  A central information source for the area of support vector machines , gaussian process prediction , mathematical programming with kernels , regularization networks , reproducing kernel hilbert spaces , and related methods. provides links to papers , upcoming
Machine learning for user modeling «
http://athos.rutgers.edu/ml4um/
Site Details: http://athos.rutgers.edu/ml4um/
  Resources for researchers and practitioners interested in the use of learning techniques in intelligent , user-adaptive systems.
Machine Learning in Games «
http://satirist.org/learn-game/
Site Details: http://satirist.org/learn-game/
  How computers can learn to get better at playing games. this site is for artificial intelligence researchers and intrepid game programmers. i describe game programs and their workings; they rely on heuristic search algorithms , neural networks , genetic al
Mixture Modelling page «
http://www.cs.monash.edu.au/~dld/mixture.modelling.page.html
Site Details: http://www.cs.monash.edu.au/~dld/mixture.modelling.page.html
  Mixture modelling , clustering , intrinsic classification , unsupervised learning and mixture modeling. links and bibliography.
Pattern Recognition Information «
http://www.ph.tn.tudelft.nl/PRInfo/index.html
Site Details: http://www.ph.tn.tudelft.nl/PRInfo/index.html
  A hub for pattern recognition linking to journals , books , bibliographies , jobs , conferences and news.
Pattern Recognition on The Web «
http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html
Site Details: http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html
  Links to various pattern recognition and machine learning resources
Programming by Example «
http://web.media.mit.edu/~lieber/PBE/index.html
Site Details: http://web.media.mit.edu/~lieber/PBE/index.html
  Programming by example (or by demonstration) is a technique for teaching the computer new behavior by demonstrating actions on concrete examples. the system records user actions and generalizes a program that can be used in new examples.

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