LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/). Update of the LIBSVM library for support vector machine algorithms (version 3.12, April 2012) [C - SVC, Epsilon-SVR, nu - SVR]. The calculations are faster. The attributes can be normalized or not. They were automatically normalized previously.
LIBCVM (http://c2inet.sce.ntu.edu.sg/ivor/cvm.html; version 2.2). Incorporation of the LIBCVM library. Two methods are available: CVM and BVM (Core Vector Machine and Ball Vector Machine). The dezscriptors can be normalized or not.
TR-IRLS (http://autonlab.org/autonweb/10538). Update of the TR-IRLS library, for the logistic regression on large dataset (large number of predictive attributes) [last available version – 2006/05/08]. The deviance is automatically provided. The display of the regression coefficients is more precise (higher number of decimals). The user can tune the learning algorithms, especially the stopping rules.
SPARSE DATA FILE. Tanagra can handle sparse data file format now (see SVMlight ou libsvm file format). The data can be used for supervised learning process or regression problem. A description of this kind of file is available on line (http://c2inet.sce.ntu.edu.sg/ivor/cvm.html).
INSTANCE SELECTION. A new component for the selection of the m first individuals among n in a branch of the diagram is available [SELECT FIRST EXAMPLES]. This option is useful when the data file is the result of the concatenation of the learning and test samples.
Download page : setup
