ELM Regularized Method for Classification Problems
| Autor: | Juan Caravaca, Mónica Millán-Giraldo, Emilio Soria-Olivas, Pablo Escandell-Montero, José M. Martínez-Martínez, Juan J Carrasco |
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| Rok vydání: | 2016 |
| Předmět: |
Wake-sleep algorithm
Computer science business.industry Training time Bayesian probability 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Regularization (mathematics) Support vector machine 010104 statistics & probability Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics business Regression problems computer Single layer Extreme learning machine |
| Zdroj: | International Journal on Artificial Intelligence Tools. 25:1550026 |
| ISSN: | 1793-6349 0218-2130 |
| DOI: | 10.1142/s0218213015500268 |
| Popis: | Extreme Learning Machine (ELM) is a recently proposed algorithm, efficient and fast for learning the parameters of single layer neural structures. One of the main problems of this algorithm is to choose the optimal architecture for a given problem solution. To solve this limitation several solutions have been proposed in the literature, including the regularization of the structure. However, to the best of our knowledge, there are no works where such adjustment is applied to classification problems in the presence of a non-linearity in the output; all published works tackle modelling or regression problems. Our proposal has been applied to a series of standard databases for the evaluation of machine learning techniques. Results obtained in terms of classification success rate and training time, are compared to the original ELM, to the well known Least Square Support Vector Machine (LS-SVM) algorithm and with two other methods based on the ELM regularization: Optimally Pruned Extreme Learning Machine (OP-ELM) and Bayesian Extreme Learning Machine (BELM). The obtained results clearly demonstrate the usefulness of the proposed method and its superiority over a classical approach. |
| Databáze: | OpenAIRE |
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