timbl 6.4.6-1build1 source package in Ubuntu

Changelog

timbl (6.4.6-1build1) wily; urgency=medium

  * No-change rebuild against libticcutils2v5

 -- Steve Langasek <email address hidden>  Fri, 07 Aug 2015 22:34:21 +0000

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Uploaded by:
Steve Langasek on 2015-08-07
Uploaded to:
Wily
Original maintainer:
Ubuntu Developers
Architectures:
any
Section:
science
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Xenial release on 2015-10-22 universe science

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File Size SHA-256 Checksum
timbl_6.4.6.orig.tar.gz 546.9 KiB 8aeb09283a3389db9b3b576b6f0632f84841facc85cad516bb57e3ec070737b4
timbl_6.4.6-1build1.debian.tar.xz 6.1 KiB 911d84f7f2221bd2cc2f0b54ff84f3301ff568c48bf327c2d0be6a4a688e51f4
timbl_6.4.6-1build1.dsc 2.2 KiB 219049193a12c6bac8ce5a84f4e018c4697f6230fbee4ecfc55f05624f90e291

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Binary packages built by this source

libtimbl4: Tilburg Memory Based Learner - runtime

 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 ILK Research Group (Tilburg University, The
 Netherlands) and the CLiPS Research Centre (University of Antwerp, Belgium).
 .
 This package provides the runtime files required to run programs that use
 TiMBL.

libtimbl4-dbgsym: No summary available for libtimbl4-dbgsym in ubuntu wily.

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timbl-dbgsym: debug symbols for package timbl

 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 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.