| Grant number: | 14/16250-9 |
| Support Opportunities: | Regular Research Grants |
| Start date: | March 01, 2015 |
| End date: | August 31, 2017 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | João Paulo Papa |
| Grantee: | João Paulo Papa |
| Host Institution: | Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil |
| City of the host institution: | Bauru |
| Associated researchers: | André Carlos Ponce de Leon Ferreira de Carvalho ; André Fujita ; Xin-She Yang |
| Associated research grant(s): | 16/50448-6 - Where do machine learning and optimization meet? (sprint 3/2016),
AP.R SPRINT 16/50022-9 - Development of cardiovascular disease and diabetes risk assessment model for ethnically diverse populations. (sprint 4/2015), AP.R SPRINT |
Abstract
Machine learning techniques have been actively employed in the last years in several research areas. Although such techniques are known for their good generalization skills, some of them are parameter-dependent, which may result in a high computational burden for learning such set of parameters, making them inviable for large-scale problems, as well as for real-time and prompt responses. This proposal aims at studying meta-heuristic optimization techniques, as well as their applications for machine learning optimization techniques, such as model and kernel selection, feature selection and information fusion, among others. This project also aims at investigating the suitability of optimizing parameters in the context of deep learning architectures, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Deep Belief Networks and Auto-encoders. The idea is to focus on multi-objective optimization techniques, since just a few number of works have addressed such context. The proposal has the collaboration of Prof. Xin-She Yang from Middlesex University, England, which is a very active researcher on meta-heuristic-based optimization. The main idea is to validate his new algorithms in the context os this project. (AU)
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