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Category:   E-Material
Subject:   TiMBL 4.0-new release of Tilburg Memory-Based Learner
From:   Antal van den Bosch
Email:   Antal.vdnBosch_(on)_kub.nl
Date received:   27 Aug 2001

---------------------------------------------------------------------- Software release: TiMBL 4.0 Tilburg Memory Based Learner ILK Research Group, http://ilk.kub.nl CNTS - Language Technology Group ---------------------------------------------------------------------- Apologies for double postings. 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 4.0. 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 with symbolic data, large numbers of features and values, and very large case bases, as typically found in natural language processing. However, TiMBL can be applied to any machine learning or data mining task for which labeled examples with fixed numbers of features are available. 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. - Speed up optimizations that enhance the underlying k-nearest neighbor classifier kernel: 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 even larger speed-ups (the IGTREE and TRIBL learning algorithms). - Verbose output to enable the monitoring of the process of extrapolation from nearest neighbors. - A multithreaded TiMBL server that can be used as a classification agent. - Fast leave-one-out testing. Version 4.0 offers a number of new features: - Class voting weighted by distance (inverse, linear, or decayed exponentially) or by user-defined exemplar weights. - Emulation of the IB2 algorithm, an incremental editing variant of IB1 (Aha, Kibler and Albert, 1991). - Internal n-fold cross-validation testing. - Various additional verbosity options, bug-fixes and code improvements. For more information: The reference guide ("TiMBL: Tilburg Memory-Based Learner, version 4.0, Reference Guide.", Walter Daelemans, Jakub Zavrel, Ko van der Sloot, and Antal van den Bosch. ILK Technical Report 01-04) can be downloaded separately and directly from http://ilk.kub.nl/do wnloads/pub/papers/ilk0104.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 http://ilk.kub.nl/ 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 Timbl_(on)_kub.nl ----------------------------------------------------------------------
 

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