timbl 6.4.13-1build1 source package in Ubuntu

Changelog

timbl (6.4.13-1build1) focal; urgency=medium

  * No-change rebuild for libgcc-s1 package name change.

 -- Matthias Klose <email address hidden>  Sun, 22 Mar 2020 16:59:44 +0100

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Uploaded by:
Matthias Klose
Uploaded to:
Focal
Original maintainer:
Debian Science Team
Architectures:
any
Section:
science
Urgency:
Medium Urgency

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Series Pocket Published Component Section
Focal release universe science

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File Size SHA-256 Checksum
timbl_6.4.13.orig.tar.gz 565.9 KiB e1a136e0f58486e1e2855b6ca528877d40d8b1e5de3c599a314ed6951d7c9e4b
timbl_6.4.13-1build1.debian.tar.xz 5.3 KiB 5d0b198dd4f3f33487bf11ac2baeae31476b218f5c9024121cb376ab40c235d3
timbl_6.4.13-1build1.dsc 2.1 KiB 2685954d92aa24fddd304d0545e46a8671934ceb89d78bb42aee0c028ca2d37f

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Binary packages built by this source

libtimbl-dev: Tilburg Memory Based Learner - development

 The Tilburg Memory Based Learner, TiMBL, is a tool for Natural Language
 Processing research, and for many other domains where classification tasks are
 learned from examples. It is an efficient implementation of k-nearest neighbor
 classifier.
 .
 TiMBL is a product of the Centre of Language and Speech Technology
 (Radboud University, Nijmegen, The Netherlands), the ILK Research Group
 (Tilburg University, The Netherlands) and the CLiPS Research Centre
 (University of Antwerp, Belgium).
 .
 This package provides the TiMBL header files required to compile C++ programs
 that use TiMBL.

libtimbl4: No summary available for libtimbl4 in ubuntu groovy.

No description available for libtimbl4 in ubuntu groovy.

libtimbl4-dbgsym: debug symbols for libtimbl4
timbl: Tilburg Memory Based Learner

 Memory-Based Learning (MBL) is a machine-learning method applicable to a wide
 range of tasks in Natural Language Processing (NLP).
 .
 The Tilburg Memory Based Learner, TiMBL, is a tool for NLP research, and for
 many other domains where classification tasks are learned from examples. It
 is an efficient implementation of k-nearest neighbor classifier.
 .
 TiMBL's features are:
  * Fast, decision-tree-based implementation of k-nearest neighbor
 classification;
  * Implementations of IB1 and IB2, IGTree, TRIBL, and TRIBL2 algorithms;
  * Similarity metrics: Overlap, MVDM, Jeffrey Divergence, Dot product, Cosine;
  * Feature weighting metrics: information gain, gain ratio, chi squared,
 shared variance;
  * Distance weighting metrics: inverse, inverse linear, exponential decay;
  * Extensive verbosity options to inspect nearest neighbor sets;
  * Server functionality and extensive API;
  * Fast leave-one-out testing and internal cross-validation;
  * and Handles user-defined example weighting.
 .
 TiMBL is a product of the Centre of Language and Speech Technology
 (Radboud University, Nijmegen, The Netherlands), the ILK Research Group
 (Tilburg University, The Netherlands) and the CLiPS Research Centre
 (University of Antwerp, Belgium).
 .
 If you do scientific research in NLP, timbl will likely be of use to you.

timbl-dbgsym: debug symbols for timbl