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 38 articles )

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  1. Fan, Wentao; Bouguila, Nizar; Chen, Yewang; Chen, Ziyi: (L_2) normalized data clustering through the Dirichlet process mixture model of von Mises distributions with localized feature selection (2020)
  2. Li, Tao; Zhang, Yi; Wang, Dingding; Xu, Jian: MCC: a multiple consensus clustering framework (2019)
  3. Aggarwal, Charu C.: Machine learning for text (2018)
  4. Qian, Pengjiang; Xi, Chen; Xu, Min; Jiang, Yizhang; Su, Kuan-Hao; Wang, Shitong; Muzic, Raymond F.: SSC-EKE: semi-supervised classification with extensive knowledge exploitation (2018)
  5. Vorontsov, Konstantin; Potapenko, Anna: Additive regularization of topic models (2015)
  6. Brucker, Florian; Benites, Fernando; Sapozhnikova, Elena: Multi-label classification and extracting predicted class hierarchies (2011)
  7. Figueiredo, Fábio; Rocha, Leonardo; Couto, Thierson; Salles, Thiago; Gonçalves, Marcos André; Meira Jr., Wagner: Word co-occurrence features for text classification (2011) ioport
  8. Andrés-Ferrer, Jesús; Juan, Alfons: Constrained domain maximum likelihood estimation for naive Bayes text classification (2010)
  9. Bouguila, Nizar: On multivariate binary data clustering and feature weighting (2010)
  10. Kuo, Tien-Fang; Yajima, Yasutoshi: Ranking and selecting terms for text categorization via SVM discriminate boundary (2010)
  11. Liu, Wenyin; Quan, Xiaojun; Feng, Min; Qiu, Bite: A short text modeling method combining semantic and statistical information (2010) ioport
  12. Qiao, Mu; Li, Jia: Two-way Gaussian mixture models for high dimensional classification (2010)
  13. Ashley, Kevin D.; Brüninghaus, Stefanie: Automatically classifying case texts and predicting outcomes (2009) ioport
  14. Bouguila, Nizar; ElGuebaly, Walid: Discrete data clustering using finite mixture models (2009)
  15. Weinberger, Kilian Q.; Saul, Lawrence K.: Distance metric learning for large margin nearest neighbor classification (2009)
  16. Wilbur, W. John; Kim, Won: The ineffectiveness of within-document term frequency in text classification (2009) ioport
  17. Yeniterzi, Süveyda; Sezerman, Osman Ugur: Enzyminer: automatic identification of protein level mutations and their impact on target enzymes from pubmed abstracts (2009) ioport
  18. Ingo Feinerer; Kurt Hornik; David Meyer: Text Mining Infrastructure in R (2008) not zbMATH
  19. 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)
  20. Diaz, Fernando: Regularizing query-based retrieval scores (2007) ioport

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