Adapted infinite kernel learning by multi-local algorithm
| Autor: | Gürkan Üstünkar, Gerhard-Wilhelm Weber, Sureyya Akyuz |
|---|---|
| Přispěvatelé: | Özyeğin University, Üstünkar, Gürkan |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Optimization
Multi-local procedure Active learning (machine learning) Computer science 0211 other engineering and technologies 02 engineering and technology Semi-supervised learning Machine learning computer.software_genre Artificial Intelligence Polynomial kernel Least squares support vector machine 0202 electrical engineering electronic engineering information engineering 021103 operations research Multiple kernel learning Support vector machines business.industry Infinite kernel learning Kernel method Computational learning theory Radial basis function kernel 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Software Simmulated annealing |
| Popis: | Due to copyright restrictions, the access to the full text of this article is only available via subscription. 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. |
| Databáze: | OpenAIRE |
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