See also: CNTS, ILK, TiMBL, Jakub Zavrel

New version of TiMBL

received from Jakub Zavrel

----------------------------------------------------------------------
 Software release:      TiMBL 3.0
                        Tilburg Memory Based Learner
                        ILK Research Group, 
                        CNTS - Language Technology Group
----------------------------------------------------------------------

The ILK (Induction of Linguistic Knowledge) Research Group at Tilburg
University, The Netherlands, and the CNTS - Language Technology Group,
at the University of Antwerp, Belgium, announce the release of a new
version of TiMBL, the "Tilburg Memory Based Learner" (version 3.0).

TiMBL is a machine learning program implementing a family of
Memory-Based Learning techniques. TiMBL stores a representation of the
training set explicitly in memory (hence `Memory Based'), and
classifies new cases by extrapolating from the most similar stored
cases.

TiMBL is being developed with a focus on classification tasks,
symbolic data, large numbers of features and values, and very large
case bases, as typically found in statistical natural language
processing. However, TiMBL can be applied for any machine learning or
data mining task that provides labeled examples with fixed numbers of
features. The main features of the system are:

- Support for symbolic, numeric and binary features.
- Automatic feature weighting. Information Gain, Gain Ratio,
  Chi-squared, and Shared Variance weighting are provided for dealing
  with features of differing importance.
- Stanfill & Waltz's / Cost & Salzberg's (Modified) Value Difference
  metric for making graded guesses of the match between two
  different symbolic values.
- Several speed up optimizations that enhance the underlying
  k-nearest neighbor classifier engine: Conversion of the flat
  instance memory into a decision tree, and inverted indexing of the
  instance memory, both yielding faster classification.
- Further compression and pruning of the decision tree, guided by
  feature information gain differences, for an even larger speed-up
  (the IGTREE and TRIBL learning algorithms).
- Verbose output to enable the monitoring of the process of
  extrapolation from nearest neighbors.

Version 3.0 offers a number of new features:

- A multithreaded TiMBL Server that can be used as a classification
  agent.
- It is seriously faster than the previous version.
- Supports different metrics per feature.
- A simplified interface to a C++ MBLClass library.
- Fast leave one out testing
- Support for sparse binary features (e.g. for text classification)
- Many bug-fixes and small improvements.

For more information: The reference guide ("TiMBL: Tilburg
Memory-Based Learner, version 3.0, Reference Guide.", Walter
Daelemans, Jakub Zavrel, Ko van der Sloot, and Antal van den
Bosch. ILK Technical Report 00-01) can be downloaded separately and
directly from

       http://ilk.kub.nl/~ilk/papers/ilk0001.ps.gz

-[download]-----------------------------------------------------------

You are invited to download the TiMBL package for educational or
non-commercial research purposes. When downloading the package you are
asked to register, and express your agreement with the license
terms. TiMBL is *not* shareware or public domain software. If you have
registered for a previous version, please be so kind to re-register
for the upgrade. TiMBL can be downloaded from

    

by following the `Software' link.

The TiMBL package contains:

- Source code (C++) with a Makefile.
- A reference guide containing descriptions of the incorporated
  algorithms, detailed descriptions of the commandline options,
  and a brief hands-on tutorial.
- Some example datasets.
- The text of the license agreement.

The package should be easy to install on most UNIX systems.

-[contact]---------------------------------------------------------

For comments and bugreports relating to TiMBL, please send mail to

       [email protected]

----------------------------------------------------------------------