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[ 原始碼: timbl  ]

套件:timbl(6.5-3)

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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.

標籤: 實做語言: C++, 角色: 程式

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硬體架構 套件大小 安裝後大小 檔案
amd64 81。2 kB245。0 kB [檔案列表]
arm64 78。2 kB233。0 kB [檔案列表]
armel 76。4 kB224。0 kB [檔案列表]
armhf 76。9 kB212。0 kB [檔案列表]
i386 81。1 kB243。0 kB [檔案列表]
mips64el 77。8 kB247。0 kB [檔案列表]
mipsel 77。9 kB241。0 kB [檔案列表]
ppc64el 79。4 kB288。0 kB [檔案列表]
s390x 78。7 kB240。0 kB [檔案列表]