Software and datasets
The AutoClass Project
: AutoClass takes a database of cases described by a combination of real
and discrete valued attributes, and automatically finds the natural
classes in that data.
Autocoder Demo (text classification)
C4.5 r8
: R. Quinlan's program for top down induction of decision trees
CBA Data mining tool
: CBA is a data mining tool developed at School of
Computing, National University of Singapore. CBA originally stands for
Classification Based on Associations. However, it turns out that it is
much more powerful than simply producing an accurate classifier for
prediction. It can also be used for mining various forms of
association rules, and for text categorization or classification.
Conx Connectionist Backprop Language and Simulator
: Conx (pronounced "kun ex") is a neural network scripting
language and environment, designed to be used by serious backprop
researchers, as well as a teaching tool for use in introductory AI
courses
DELVE  Data for Evaluating Learning in Valid Experiments
: Delve is a standardised environment designed to evaluate the
performance of methods that learn relationships based primarily on
empirical data. Delve makes it possible for users to compare their
learning methods with other methods on many datasets. The Delve
learning methods and evaluation procedures are well documented, such
that meaningful comparisons can be made.
FastICA package for MATLAB
: the FastICA package is a publicdomain MATLAB program that implements
the fast fixedpoint algorithm
for independent component analysis and projection
pursuit. It features an easytouse graphical user interface, and a
computationally powerful algorithm.
FFOIL r2
: R. Quinlan's program for inductive logic programming
FOIL r6
LIBSVM by ChihChung Chang and ChihJen Lin
: LIBSVM is an integrated tool for support vector
classification, (CSVC, nuSVC ), regression
(epsilonSVR, nuSVR) and distribution
estimation (oneclass SVM ). It supports
multiclass classification. The basic algorithm is a simplification of
both SMO
by Platt and
SVMLight
by Joachims. It is also a
simplification of the modification2
of SMO by Keerthi et al.
MLC++ Home Page (SGI)
: MLC++ is a library of C++ classes for supervised machine
learning. MLC++ was initially developed at Stanford
University and is now distributed by SGI.
Microsoft Belief Network Tools
: an application developed by the Decision Theory Adaptive Systems
Group within Microsoft Research. It allows the creation, assessment
and evaluation of Bayesian belief networks.
Software by Radford Neal Available OnLine
: Flexible Bayesian modeling and Markov chain sampling, Low Density
Parity Check (LDPC) codes, arithmetic coding for data compression.
Neural Network Toolbox for MATLAB
: this toolbox provides a complete set of functions and a graphical user
interface for the design, implementation, visualization, and
simulation of neural networks. It supports the most commonly used
supervised and unsupervised network architectures and a comprehensive
set of training and learning functions
Neural Networks at your Fingertips
: simulator for Adaline, Backprop, Hopfield nets, Bidirectional
Associative Memories, Boltzman Machine, Counterpropagation, Selforganizing
maps, Adaptive Resonance Theory
NeuroForecaster GENETICA
: full 32bit implementation for Windows for generalpurpose business
and financial forecasting. Performs timeseries analysis,
crosssectional classification and indicator analysis.
NEURON
: NEURON is a simulation environment for developing and exercising
models of neurons and networks of neurons. It is particularly
wellsuited to problems where cable properties of cells play an
important role, possibly including extracellular potential close to
the membrane), and where cell membrane properties are complex,
involving many ionspecific channels, ion accumulation, and second
messengers. It evolved from a long collaboration between Michael Hines
and John W. Moore at the Department of Neurobiology, Duke University.
The NICO ANN Toolkit Home Page
: the NICO Toolkit is an artificial neural network toolkit designed and
optimized for speech technology applications. It is easy to construct
neural networks with both recurrent connections and/or timedelay
windows to capture temporal features. The network topology is very
flexible  any number of layers is allowed, and layers can be
arbitrarily connected. Powerful tools for sparse connectivity are also
included. Tools for extracting inputfeatures from the speech signal
are also part of the toolkit, as well as tools for computing target
values from many common phonetic labelfile formats.
The NN learning algorithm benchmarking page
: proper benchmarking of (neural network and other) learning
architectures is a prerequisite for orderly progress in this field. In
many published papers deficiencies can be observed in the benchmarking
that is performed. A workshop about NN benchmarking at NIPS*95
addressed the status quo of benchmarking, common errors and how to
avoid them, currently existing benchmark collections, and, most
prominently, a new benchmarking facility including a results database.
This page contains pointers to written versions or slides of most of
the talks given at the workshop plus some related material. The page
is intended to be a repository for such information to be used as a
reference by researchers in the field.
The NNCTRL Toolkit. Neural networks for control
: the NNCTRL toolkit is a set of tools for design and simulation of
control systems based on neural networks. The toolkit is an addon to
the NNSYSID toolbox,
which is a toolbox for system identification with neural
networks. Version 2 requires MATLAB 5.3 or higher. For MATLAB
4.2MATLAB 5.2 it is possible to use the old Version 1. The toolkit
contains: Control by feedback linearization. Direct inverse
control. Internal model control. Optimal control. Control
using instantaneous linearization (includes approximate pole
placement, approximate minimum variance and approximate GPC control). Nonlinear
Generalized Predictive Control. Nonlinear Feedforward Control.
PDP++ Home Page
: the PDP++ software is a neuralnetwork simulation system written in
C++. It represents the next generation of the PDP software originally
released with the McClelland and Rumelhart "Explorations in
Parallel Distributed Processing Handbook", MIT Press, 1987. It is
easy enough for novice users, but very powerful and flexible for
research use.
Old PDP package
The Perceptron
: a simple simulator for the Perceptron learning rule
PlaNet5.7
Pygmalion
R: the Comprehensive R Archive Network
: R is `GNU S', a freely available language and environment for
statistical computing and graphics which provides a wide variety of
statistical and graphical techniques: linear and nonlinear modeling,
statistical tests, time series analysis, classification, clustering, etc.
RuleQuest Research Data Mining Tools (C5.0, Magnum Opus)
SNNS Stuttgart Neural Network Simulator
Spikeneuralog
SOM_PAK, LVQ_PAK
SOM Toolbox for Matlab
SUBDUE Knowledge Discovery in Structural Databases
StatLog: Evaluation  Characterization of Classification Algorithms
: this work was supported by Esprit Project 5170 StatLog
(199194). This project was concerned with comparative studies of
different machine learning, neural and statistical classification
algorithms. About 20 different algorithms were evaluated on more than
20 different datasets. The tests carried out under this project
produced many interesting results. Site contains datasets and
algorithms
SVMLight Support Vector Machine
: SVM^{light} is an implementation of
Support Vector Machines (SVMs) in C written by T. Joachims
TiMBL: Tilburg Memory Based Learner
: TiMBL is a program implementing several MemoryBased
Learning techniques. TiMBL stores a representation of the training set
explicitly in memory, and classifies new cases by extrapolation from
the most similar stored cases. Several metrics and algorithms are
implemented in TiMBL; among others: Information Gain weighting for
dealing with features of differing importance (IB1IG), and the
Modified Value Difference metric for making graded guesses of the
match between two different symbolic values. TiMBL is optimized for
fast classification by using several indexing techniques and heuristic
approximations (such as IGTREE and TRIBL).
Tlearn software page
UC Irvine KDD Archive
UC Irvine Machine Learning Datasets Repository
UC Irvine Machine Learning Programs
WEKA Machine Learning Project
: several standard ML techniques into a software "workbench"
called WEKA, for Waikato Environment for Knowledge Analysis
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