CRF-package {CRF}R Documentation

CRF - Conditional Random Fields

Description

Library of Conditional Random Fields model

Details

CRF is R package for various computational tasks of conditional random fields as well as other probabilistic undirected graphical models of discrete data with pairwise and unary potentials. The decoding/inference/sampling tasks are implemented for general discrete undirected graphical models with pairwise potentials. The training task is less general, focusing on conditional random fields with log-linear potentials and a fixed structure. The code is written entirely in R and C++. The initial version is ported from UGM written by Mark Schmidt.

Decoding: Computing the most likely configuration

Inference: Computing the partition function and marginal probabilities

Sampling: Generating samples from the distribution

Training: Given data, computing the most likely estimates of the parameters

Tools: Tools for building and manipulating CRF data

Author(s)

Ling-Yun Wu wulingyun@gmail.com

References

J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In the proceedings of International Conference on Machine Learning (ICML), pp. 282-289, 2001.

Mark Schmidt. UGM: A Matlab toolbox for probabilistic undirected graphical models. http://www.cs.ubc.ca/~schmidtm/Software/UGM.html, 2007.

Examples


library(CRF)
data(Small)
decode.exact(Small$crf)
infer.exact(Small$crf)
sample.exact(Small$crf, 100)


[Package CRF version 0.4-3 Index]