Multilayer Radial Basis Function Kernel Machine
| Autor: | Mashail Alsalamah, Saad Amin |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Radial basis function network
Computational complexity theory Computer science business.industry Deep learning Dimensionality reduction Feature selection 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Kernel (statistics) Radial basis function kernel 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Radial basis function Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783319588766 MobiHealth |
| DOI: | 10.1007/978-3-319-58877-3_34 |
| Popis: | Radial Basis Function (RBF) Kernel Machines have become commonly used in Machine Learning tasks, but they contain certain flaws (e.g., some suffer from fast growth in the number of learning parameters while predicting data with large number of variations). Besides, Kernel Machines with single hidden layers lack mechanisms for feature selection in multidimensional data space, and machine learning tasks become intractable. This paper investigates “deep learning” architecture composed of multilayered adaptive non-linear components – Multilayer RBF Kernel Machine – to address RBF limitations. Three different approaches of features selection and dimensionality reduction to train RBF based on Multilayer Kernel Learning are explored, and comparisons made between them in terms of accuracy, performance and computational complexity. Results show that the multilayered system produces better results than single-layer architecture. In particular, developing decision support system in term of data mining. |
| Databáze: | OpenAIRE |
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