Projects authored by rui xia.


Logo Naive Bayes EM Algorithm 1.0.0

by openpr_nlpr - December 2, 2011, 05:35:09 CET [ Project Homepage BibTeX Download ] 2250 views, 500 downloads, 1 subscription

About: OpenPR-NBEM is an C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. OpenPR-NBEM uses the multinomial event model for representation. The maximum likelihood estimate is used for supervised learning, and the expectation-maximization estimate is used for semi-supervised and un-supervised learning.

Changes:

Initial Announcement on mloss.org.


Logo Linear Discriminant Function Classifier 1.0.0

by openpr_nlpr - December 2, 2011, 05:27:27 CET [ Project Homepage BibTeX Download ] 1141 views, 302 downloads, 1 subscription

About: This program is a C++ implementation of Linear Discriminant Function Classifier. Discriminant functions such as perceptron criterion, cross entropy (CE) criterion, and least mean square (LMS) criterion (all for multi-class classification problems) are supported in it. The program uses a sparse-data structure to represent the feature vector to seek higher computational speed. Some other techniques such as online updating, weights averaging, gaussian prior regularization are also supported.

Changes:

Initial Announcement on mloss.org.


Logo Naive Bayes Classifier 1.0.0

by openpr_nlpr - December 2, 2011, 05:25:44 CET [ Project Homepage BibTeX Download ] 2011 views, 508 downloads, 1 subscription

About: This program is a C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. The program uses the multinomial event model for representation, the maximum likelihood estimate with a Laplace smoothing technique for learning parameters. A sparse-data structure is defined to represent the feature vector in the program to seek higher computational speed.

Changes:

Initial Announcement on mloss.org.