Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, tes...
Abstract. We propose an algorithm for Sparse Bayesian Classification for multi-class problems using Automatic Relevance Determination(ARD). Unlike other approaches which treat mult...
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
Abstract. Several algorithms, namely CubeMiner, Trias, and DataPeeler, have been recently proposed to mine closed patterns in ternary relations. We consider here the specific cont...
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...