Resources for Neural Networks and Knowledge Modeling

Resources

Introduction to Neural Nets

Basic introduction to Neural Networks including an animated presentation of different Networks learning process. Easy to understand.

http://www.makhfi.com/tutorial/introduction.htm

AI FAQ/general

Answer to certain questions and topics that come up frequently in the various network discussion groups

http://www.faqs.org/faqs/ai-faq/general/

AI-faq/neural-nets

Answer to certain questions and topics that come up frequently in the Neural Nets discussion groups

ftp://ftp.sas.com/pub/neural/FAQ.html

International Neural Network Society

The International Neural Network Society (INNS) is the premiere organization for individuals interested in a theoretical and computational understanding of the brain and applying that knowledge to develop new and more effective forms of machine intelligence.

http://www.inns.org/

Introduction to Neural Networks

This article describes how to implement an artificial neural network
that is capable of being trained to recognize patterns.

http://www.colinfahey.com/2003apr20_neuron/2003apr20_neuron.htm

Introduction to Genetic Algorithms

The site introduces some of the fundamentals of genetics algorithms. It is intended to be used for learning genetics algorithms without any previous knowledge of that subject.

http://cs.felk.cvut.cz/~xobitko/ga/index.html

Neural Network sample training data from UCI

This is a repository of datasets to be used for neural network training.
Covers more 10 different problem areas.

ftp://ftp.ira.uka.de/pub/neuron/proben1.tar.gz

Introduction to Neural Networks

A well written introduction on artificial neural network and their applications.

http://documents.wolfram.com/applications/neuralnetworks/index.html

Introduction to Neural Networks & Learning Methods

A very good introduction to artificial neural network and their learning methods.

http://fibo.kmutt.ac.th/publication/thai/article/intro_nn.html

Introduction to Neural Networks from Department of Defense

An excellent introduction to the basic principles of neural networks.

http://www.dacs.dtic.mil/techs/neural/neural_ToC.html

Introduction to Self-Organising Maps

An excellent introduction to the self-organized Maps and Pattern Recognition

http://soft.ee.umist.ac.uk/nigel/neural/SOMcon.html

Introduction to Support Vector Machine (SVM)

A short introduction to Support Vector Machine

http://kiew.cs.uni-dortmund.de:8001/mlnet/instances/81d91eaa-da14013dc6

Neural Networks Books

A total of 349 Neural Networks items sorted by poularity. Includes reviews, product descriptions, prices, and buying information.

http://www.rbookshop.com/computer_books/n/Neural_Networks/Neural_Networks_Books3.htm

The society for ANN in Medicine and Biology

A collection of links to people and organisations working with medical applications of artificial neural networks and related techniques.

http://www.phil.gu.se/ann/main.html

The Kernel Machines

A Site dedicated to Kernel Machines and related methods. But also Gaussian Process prediction, Mathematical Programming with Kernels, Regularization Networks and Reproducing Kernel Hilbert Spaces.

http://www.kernel-machines.org/

Neural Network Companies

A Site will reference to companies with products based on Neural Networks.

http://www.it.uom.gr/pdp/DigitalLib/Neural/Neu_comp.htm

Information on over 50 NN applications

BrainMaker Neural Network Software: Great list of examples of specific NN applications regarding stocks, business, medicine, and manufacturing.

http://www.calsci.com/Applications.html

Neural Networks Tutorial with Java Applets

This site includes a series of exercises and demos. Each exercise consists of a short introduction, a small demonstration program written in Java (Java Applet), and a series of questions which are intended as an invitation to play with the programs and explore the possibilities of different algorithms.

http://diwww.epfl.ch/mantra/tutorial/english/

Neural Networks Introduction

NEURAL NETWORK DESIGN provides a clear and detailed survey of fundamental neural network architectures and learning rules. In it, the authors emphasize mathematical analysis of networks, methods for training networks, and application of networks to practical engineering problems in pattern recognition, signal processing, and control systems.

http://hagan.ecen.ceat.okstate.edu/nnd.html

Neural Networks Innovations and Patents

Recent U.S. patents related to Neural Networks

http://www.prime-radiant.com/technologies/Neural_Networks.html

Neural Networks & AI for Gaming

Jay Scott resources site for Machine Learning for Gaming. He describes game programs and their workings; they rely on heuristic search algorithms, neural networks, genetic algorithms, temporal differences, and other methods.

http://satirist.org/learn-game/index.html

NeuroEvolution of Augmenting Topologies (NEAT)

NEAT is a genetic algorithm for evolving neural networks written by Ken Stanley at University of Texas at Austin and published under the GPL.

http://nn.cs.utexas.edu/project-view.php?RECORD_KEY(Projects)=ProjID&ProjID(Projects)=14

SharpNEAT (C# implementation of NEAT)

NEAT is a technique for evolving neural net structure and connection weights, SharpNEAT is one of a number of implementations but happens to be written in C#

http://www.cgreen.dsl.pipex.com/

NEOXI Neural Network Resources

Content: Professionally selected extensive collection of neural network resources.
Audience: Communities of commerce, industry, academics, engineers, practitioners, and individuals interested in neural networks, machine learning, data mining, artificial intelligence, soft-computing, and numerous other fields directly or indirectly utilizing the neural network technology.

http://www.neoxi.com

The Future Of Software

New research in artificial intelligence could lay the groundwork for computer systems that learn from their users and the world around them. Artificial intelligence, a field that has tantalized social scientists and high-tech researchers since the dawn of the computer industry, had lost its sex appeal by the start of the last decade.
Now a new generation of researchers hopes to rekindle interest in AI. Faster and cheaper computer processing power, memory, and storage, and the rise of statistical techniques for analyzing speech, handwriting, and the structure of written texts, are helping spur new developments, as is the willingness of today's practitioners to trade perfection for practical solutions to everyday problems.

http://www.informationweek.com/story/showArticle.jhtml?articleID=161501161&tid=5979

Neural Network Programming in Java (by Jeff Heaton)

Programming Neural Networks in Java will show the intermediate to advanced Java programmer how to create neural networks. This book attempts to teach neural network programming through two mechanisms. First the reader is shown how to create a reusable neural network package that could be used in any Java program. Second, this reusable neural network package is applied to several real world problems that are commonly faced by IS programmers. This book covers such topics as Kohonen neural networks, multi layer neural networks, training, back propagation, and many other topics.

http://www.heatonresearch.com/articles/series/1/