Bow: A Toolkit for Statistical Language Modeling, Text Retrieval, Classification and Clustering. Bow (or libbow) is a library of C code useful for writing statistical text analysis, language modeling and information retrieval programs. The current distribution includes the library, as well as front-ends for document classification (rainbow), document retrieval (arrow) and document clustering (crossbow).

References in zbMATH (referenced in 30 articles )

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  1. Vorontsov, Konstantin; Potapenko, Anna: Additive regularization of topic models (2015)
  2. Brucker, Florian; Benites, Fernando; Sapozhnikova, Elena: Multi-label classification and extracting predicted class hierarchies (2011)
  3. Figueiredo, Fábio; Rocha, Leonardo; Couto, Thierson; Salles, Thiago; Gonçalves, Marcos André; Meira Jr., Wagner: Word co-occurrence features for text classification (2011)
  4. Andrés-Ferrer, Jesús; Juan, Alfons: Constrained domain maximum likelihood estimation for naive Bayes text classification (2010)
  5. Bouguila, Nizar: On multivariate binary data clustering and feature weighting (2010)
  6. Liu, Wenyin; Quan, Xiaojun; Feng, Min; Qiu, Bite: A short text modeling method combining semantic and statistical information (2010)
  7. Ashley, Kevin D.; Brüninghaus, Stefanie: Automatically classifying case texts and predicting outcomes (2009)
  8. Bouguila, Nizar; ElGuebaly, Walid: Discrete data clustering using finite mixture models (2009)
  9. Weinberger, Kilian Q.; Saul, Lawrence K.: Distance metric learning for large margin nearest neighbor classification (2009)
  10. Wilbur, W.John; Kim, Won: The ineffectiveness of within-document term frequency in text classification (2009)
  11. Jing, Liping; Li, Junjie; Ng, Michael K.; Cheung, Yiu-Ming; Huang, Joshua: SMART: a subspace clustering algorithm that automatically identifies the appropriate number of clusters (2008)
  12. Diaz, Fernando: Regularizing query-based retrieval scores (2007)
  13. Peng, Jiming; Wei, Yu: Approximating $k$-means-type clustering via semidefinite programming (2007)
  14. Kim, Jaehwan; Choi, Seungjin: Semidefinite spectral clustering (2006)
  15. Li, Jia; Zha, Hongyuan: Two-way Poisson mixture models for simultaneous document classification and word clustering (2006)
  16. Li, Tao: A unified view on clustering binary data (2006)
  17. Li, Tao; Zhu, Shenghuo; Ogihara, Mitsunori: Using discriminant analysis for multi-class classification: an experimental investigation (2006)
  18. Li, Tao; Zhu, Shenghuo; Ogihara, Mitsunori: Using discriminant analysis for multi-class classification: An experimental investigation (2006)
  19. Zhong, Shi: Semi-supervised model-based document clustering: A comparative study (2006)
  20. Keerthi, S.Sathiya; Decoste, Dennis: A modified finite Newton method for fast solution of large scale linear SVMs (2005)

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