In analyzing my data I wanted to classify it with a naive Bayesian classifier. I wasn't sure I had the math right, so I wrote a tiny abstract classifier to test with. The code is pretty cool:
AI Related Ruby Extensions This page will maintain list of AI related extensions/modules/gems for the Ruby programming language. Please contact me if you know something I missed.
If you are starting with Neural Networks you should check out my online book on the subject. It contains over 300 pages of information on Neural Network Programming in Java. You can access it here.
This project contains Naive and Fishers bayesian classifiers, as described in Toby Segaran's book "Programming Collective Intelligence." The book has python implementations; this is a Java implementation.
Emergent (a major rewrite of PDP++) is a comprehensive simulation environment for creating complex, sophisticated models of the brain and cognitive processes using neural network models.
My primary research interests are concentrated in the areas of bioinformatics, data mining, and parallel processing, and from time-to-time I look at various problems in the areas of information retrieval, collaborative filtering, and electronic design aut
Bow (or libbow) is a library of C code useful for writing statistical text analysis, language modeling and information retrieval programs. The current distribution includes the library, as well as front-ends for document classification (rainbow), document
The NetAnalyzer Project drives open source development of interactive control, debugging, analysis, and visualization tools for Hierarchical Temporal Memory (HTM).
This relates to the recent Slashdot-posted paper about the world being a VR. If indeed human mind is non-computable, the world can't be VR. Cf. On Intelligence.
A. Newell, and H. Simon. Communications of the ACM, 19 (3):
113-126(March 1976)p. 116:
"The Physical Symbol System Hypothesis. A physical
symbol system has the necessary and sufficient
means for general intelligent action."
p. 120:
"Heuristic Search Hypothesis. The solutions to
problems are represented as symbol structures.
A physical symbol system exercises its intelligence
in problem solving by search--that is, by
generating and progressively modifying symbol
structures until it produces a solution structure."
p. 121:
"To state a problem is to designate (1) a test
for a class of symbol structures (solutions of the
problem), and (2) a generator of symbol structures
(potential solutions). To solve a problem is
to generate a structure, using (2), that satisfies
the test of (1).".