This project contains Naive and Fishers bayesian classifiers, as described in Toby Segaran's book "Programming Collective Intelligence." The book has python implementations; this is a Java implementation.
You've built a vibrant community of Family Guy enthusiasts. The SVD recommendation algorithm took your site to the next level by allowing you to leverage the implicit knowledge of your community. But now you're ready for the next iteration - you are about
"The future co-existence of controlled vocabularies and collaborative tagging is predicted, with each appropriate for use within distinct information contexts: formal and informal."
LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM.
Main features of LIBLINEAR include
* Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
* Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
* Cross validation for model selection
* Probability estimates (logistic regression only)
* Weights for unbalanced data
* MATLAB/Octave, Java interfaces
Libtextcat is a library with functions that implement the classification technique described in Cavnar & Trenkle, "N-Gram-Based Text Categorization" [1]. It was primarily developed for language guessing, a task on which it is known to perform with near-pe
W. Martins, M. Goncalves, A. Laender, and G. Pappa. Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, page 193--202. New York, NY, USA, ACM, (2009)