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.
Con-x
Connectionist Backprop
Language and Simulator
: Con-x (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 public-domain
MATLAB program that implements
the fast fixed-point
algorithm for independent
component analysis and
projection pursuit. It
features an easy-to-use
graphical user interface,
and a computationally
powerful algorithm.
FFOIL r2
: R. Quinlan's program
for inductive logic programming
FOIL r6
LIBSVM by Chih-Chung
Chang and Chih-Jen Lin
: LIBSVM is an integrated
tool for support vector
classification, (C-SVC,
nu-SVC
), regression (epsilon-SVR,
nu-SVR)
and distribution estimation
(one-class
SVM ). It supports
multi-class 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
On-Line : 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, Self-organizing
maps, Adaptive Resonance
Theory
NeuroForecaster
GENETICA : full 32-bit
implementation for Windows
for general-purpose business
and financial forecasting.
Performs time-series analysis,
cross-sectional classification
and indicator analysis.
NEURON
: NEURON is a simulation
environment for developing
and exercising models
of neurons and networks
of neurons. It is particularly
well-suited 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 ion-specific
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 time-delay 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 input-features
from the speech signal
are also part of the toolkit,
as well as tools for computing
target values from many
common phonetic label-file
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 add-on to the NNSYSID
toolbox, which is
a toolbox for system identification
with neural networks.
Version 2 requires MATLAB
5.3 or higher. For MATLAB
4.2-MATLAB 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 neural-network 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
Spike-neuralog
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 (1991-94). 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
SVM-Light Support Vector
Machine : SVMlight
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 Memory-Based 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 (IB1-IG), 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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