![]() This parameter can be used to speed up computations. ![]() Tolerance: This value define the tolerance when comparing 2 values during the optimization.Nu: This value is the regularization parameter and is between 0 and 1 (see the description for more details).SMO parameters: This option allows you to tune the optimization algorithm to your specific needs. Options of One-class Support Vector Machine function in XLSTAT Besides, most of the training data must belong to the positive class while the volume of envelope is minimal.Īs others SVM methods available in XLSTAT the optimization problem is solved thanks to the Sequential Minimal Optimization (SMO) using second order information as proposed by Fan and Al. The aim is to seperate data into two classes (based on a decision function), the positive one considered as the class of inliers and the negative one considered as the class of outliers. The One-class Support Vector Machine (One-class SVM) algorithm seeks to envelop underlying inliers. It was in 1999 that Schölkopf et al. proposed an expansion to SVM for the unsupervised learning and more precisely for novelty detection. What is One-class Support Vector Machine?
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |