Adapted Infinite Kernel Learning by Multi-Local Algorithm
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, vol.30, no.4, 2016 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 30 Issue: 4
- Publication Date: 2016
- Doi Number: 10.1142/s0218001416510046
- Journal Name: INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Keywords: Infinite kernel learning, support vector machines, optimization, multi-local procedure, multiple kernel learning, simmulated annealing
- Middle East Technical University Affiliated: Yes
Abstract
The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, observed margin strategy is integrated into our novel infinite kernel learning (IKL) algorithm together with multi-local procedure (MLP) which is an optimization technique to find global solution. The experimental results show improvements in accuracy and speed when comparing with multiple kernel learning (MKL) and semi-infinite linear programming (SILP) with CV.