Low-memory Multinomial Logistic Regression with Support for Text Classification

Description

maxent is an R package with tools for low-memory multinomial logistic regression, also known as maximum entropy. The focus of this maximum entropy classifier is to minimize memory consumption on very large datasets, particularly sparse document-term matrices represented by the tm package. The library is built on top of an efficient C++ implementation written by Yoshimasa Tsuruoka.

Details

Author(s)

Timothy P. Jurka <tpjurka@ucdavis.edu>

References

Y. Tsuruoka. "A simple C++ library for maximum entropy classification." University of Tokyo Department of Computer Science (Tsujii Laboratory), 2011. URL http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/maxent/.

Examples

# LOAD LIBRARY
library(maxent)

# READ THE DATA, PREPARE THE CORPUS, and CREATE THE MATRIX
data <- read.csv(system.file("data/NYTimes.csv.gz",package="maxent"))
corpus <- Corpus(VectorSource(data$Title[1:150]))
matrix <- DocumentTermMatrix(corpus)

# TRAIN/PREDICT USING SPARSEM REPRESENTATION
sparse <- as.compressed.matrix(matrix)
model <- maxent(sparse[1:100,],data$Topic.Code[1:100])
results <- predict(model,sparse[101:150,])

Results