Finding input sub-spaces for polymorphic fuzzy signatures
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Gaze pattern and reading comprehension
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Reading your mind: EEG during reading task
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Complex Structured Decision Making Model: A hierarchical frame work for complex structured data
Information Sciences: an International Journal
Modeling the mental differentiation task with EEG
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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We previously introduced the generalized Weighted Relevance Aggregation Operators (WRAO) for hierarchical fuzzy signatures. WRAO enhances the ability of the fuzzy signature model to adapt to different applications and simplifies the learning of fuzzy signature models from data. In this paper we overcome the practical issues which occur when learning WRAO from data. This paper discuss an algorithm for learning WRAO using the Levenberg- Marquardt (LM) method, which is one of the most sophisticated and widely used gradient based optimization method. Also, this paper shows the successful results of applying the proposed algorithm to extract WRAO for two real world problems namely High Salary Selection and SARS Patient Classification.