Artificial Intelligent Systems and Machine Learning

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Classifying the Depression Data Polynomial Discriminant Vectors


Affiliations
1 Department of Computer Science and Engineering, Alagappa University, Karaikudi, India
2 Computer Center, Alagappa University, Karaikudi, India
3 Udaya School of Engineering, 629204, India
     

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This paper discusses the preprocessing and classification of depression data using back propagation algorithm (BPA). In general, input vectors will not be orthogonal to each other. The proposed method of preprocessing the input vector makes possible BPA learn the input vectors. The classification performance of BPA have been shown for a minimum 80%.

Keywords

Depression Data, Back Propagation Algorithm, Polynomial Discriminant Vector (PDV).
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  • Classifying the Depression Data Polynomial Discriminant Vectors

Abstract Views: 204  |  PDF Views: 3

Authors

P. Radha
Department of Computer Science and Engineering, Alagappa University, Karaikudi, India
E. Ramaraj
Computer Center, Alagappa University, Karaikudi, India
S. Purushothaman
Udaya School of Engineering, 629204, India

Abstract


This paper discusses the preprocessing and classification of depression data using back propagation algorithm (BPA). In general, input vectors will not be orthogonal to each other. The proposed method of preprocessing the input vector makes possible BPA learn the input vectors. The classification performance of BPA have been shown for a minimum 80%.

Keywords


Depression Data, Back Propagation Algorithm, Polynomial Discriminant Vector (PDV).