MINING MASSIVE DATA SETS FOR SECURITY
Tahun : 2008Pengarang : F. Fogelman-Soulié et al
Penerbit : IOS Press
Ket : The classical setting of the supervised learning problem is to learn a decision rule from labeled data where data is points in some space X and labels are in {+1, −1}. In this paper we consider an extension of this supervised learning setting: given training vectors in space X along with labels and description of this data in another space X∗, find in space X a decision rule better than the one found in the classical setting [1]. Thus, in this setting we use two spaces for describing the training data but the test data is given only in the space X. In this paper, using SVM type algorithms, we demonstrate the potential advantage of the new setting
Ketegori : DATA SECURITY
