Infinite Kernel Learning by Semi-infinte Optimization

A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to ¿learn¿. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). As a first motivation, we develop a model selection tool induced into SVM in order to solve a particular problem of computational biology which is prediction of eukaryotic pro-peptide cleavage site applied on the real data collected from NCBI data bank. Based on our biological example, a generalized model selection method is employed as a generalization for all kinds of learning problems. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. Convex combinations of kernels were developed to classify this kind of data. Nevertheless, selection of the finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of ¿infinite¿ kernel combinations for learning problems with the help of infinite and semi-infinite programming.