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| Author(s): |
André Luis Debiaso Rossi
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2009-04-01 |
| Examining board members: |
André Carlos Ponce de Leon Ferreira de Carvalho;
Ronaldo Cristiano Prati;
Ivan Nunes da Silva
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| Advisor: | André Carlos Ponce de Leon Ferreira de Carvalho |
| Abstract | |
Machine learning is a research area whose main goal is to design computational systems capable of learning through experience. Many machine learning techniques have free parameters whose values are generally defined by the user. Usually, these values affect the knowledge acquisition process directly, resulting in different models. Recently, bioinspired optimization algorithms have been successfully applied to the parameter tuning of machine learning techniques. These techniques may present variable sensitivity to the selection of the values of its parameters and different parameter tuning algorithms may present different behaviors. This thesis investigates the use of bioinspired algorithms for the parameter tuning of artificial neural networks and support vector machines in classification problems. The goal of this thesis is to investigate which techniques benefits most from parameter tuning and which are the most efficient algorithms to use with these techniques. Experimental results show that these bioinspired algorithms can find better classifiers when compared to other approaches. However, this improvement is statistically significant only to some datasets. It was possible to verify that the use of standard parameter values for the classification techniques leads to similar performances to those obtained with the bioinspired algorithms. However, for some datasets, the parameter tuning may significantly improve a classifier performance (AU) | |