maxent: An R package for low-memory multinomial logistic regression with support for semi-automated text classification

R Journal

Volumn 4, Issue 1, 2012, Pages 56-59

  Jurka, Timothy P ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84883212771     PISSN: None     EISSN: 20734859     Source Type: Journal    
DOI: 10.32614/rj-2012-007     Document Type: Article

References (9)

1. E. Adler, J.Wilkerson. Congressional Bills Project. University of Washington, 2004. URL http://www.congressionalbills.org/.
2. L. Bouttou, O. Bousquet. The tradeoffs of large scale learning. Advances in Neural Information Processing Systems, pp. 161-168, 2008. URL http://leon.bottou.org/publications/pdf/nips-2007.pdf.
3. A. Boydstun. How Policy Issues Become Front-Page News. Under review. URL http://psfaculty. ucdavis.edu/boydstun/.
4. D. Eddelbuettel, R. Francois. Rcpp: Seamless R and C++ integration. Journal of Statistical Software, 40(8), pp. 1-18, 2011. URL http://www.jstatsoft. org/v40/i08/.
5. I. Feinerer, K. Hornik, D. Meyer. Text mining infrastructure in R. Journal of Statistical Software, 25(5), pp. 1-54, 2008. URL http://www.jstatsoft.org/ v25/i05/.
6. R. Koenker, P. Ng. SparseM: A Sparse Matrix Package for R. CRAN Package Archive, 2011. URL http://cran.r-project.org/package=SparseM.
7. R. Nocedal. The performance of several algorithms for large scale unconstrained optimization. Large Scale Numerical Optimization, pp. 138-151, 1990. Society for Industrial & Applied Mathematics.
8. K. Nigam, J. Lafferty, and A. McCallum. Using maximum entropy for text classification. IJCAI-99Workshop on Machine Learning for Information Filtering, pp. 61-67, 1999. URL http://www.kamalnigam. com/papers/maxent-ijcaiws99.pdf.
9. 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/.