The present work is dedicated to the study of modes of data-presentation in the range between text and informant within the framework of inductive inference. In this study, the le...
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...
Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning ap...
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...