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Hybrid Neural Architecture for Intelligent Recommender System Classification Unit Design

Hybrid Neural Architecture for Intelligent Recommender System Classification Unit Design

Emmanuel Buabin
Copyright: © 2013 |Pages: 22
ISBN13: 9781466625426|ISBN10: 1466625422|EISBN13: 9781466625433
DOI: 10.4018/978-1-4666-2542-6.ch010
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MLA

Buabin, Emmanuel. "Hybrid Neural Architecture for Intelligent Recommender System Classification Unit Design." Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods, edited by Satchidananda Dehuri, et al., IGI Global, 2013, pp. 192-213. https://doi.org/10.4018/978-1-4666-2542-6.ch010

APA

Buabin, E. (2013). Hybrid Neural Architecture for Intelligent Recommender System Classification Unit Design. In S. Dehuri, M. Patra, B. Misra, & A. Jagadev (Eds.), Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods (pp. 192-213). IGI Global. https://doi.org/10.4018/978-1-4666-2542-6.ch010

Chicago

Buabin, Emmanuel. "Hybrid Neural Architecture for Intelligent Recommender System Classification Unit Design." In Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods, edited by Satchidananda Dehuri, et al., 192-213. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2542-6.ch010

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Abstract

The objective is intelligent recommender system classification unit design using hybrid neural techniques. In particular, a neuroscience-based hybrid neural by Buabin (2011a) is introduced, explained, and examined for its potential in real world text document classification on the modapte version of the Reuters news text corpus. The so described neuroscience model (termed Hy-RNC) is fully integrated with a novel boosting algorithm to augment text document classification purposes. Hy-RNC outperforms existing works and opens up an entirely new research field in the area of machine learning. The main contribution of this book chapter is the provision of a step-by-step approach to modeling the hybrid system using underlying concepts such as boosting algorithms, recurrent neural networks, and hybrid neural systems. Results attained in the experiments show impressive performance by the hybrid neural classifier even with a minimal number of neurons in constituting structures.

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