Adapted Infinite Kernel Learning by Multi-Local Algorithm

Date

2016-05-01

Author

Akyuz, Sureyya Ozogur
Ustunkar, Gurkan
Weber, Gerhard Wilhelm

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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.

Subject Keywords

Infinite Kernel Learning, Support Vector Machines, Optimization, Multi-Local Procedure, Multiple Kernel Learning, Simmulated Annealing

URI

https://hdl.handle.net/11511/56478

Journal

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

DOI

https://doi.org/10.1142/s0218001416510046

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

S. O. Akyuz, G. Ustunkar, and G. W. Weber, “Adapted Infinite Kernel Learning by Multi-Local Algorithm,” INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, pp. 0–0, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56478.