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Start Over Search Limiters Available in Library Collection Topic authors Publication Type Periodicals Journal ieee transactions on neural networks & learning systems Publisher ieee
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33. IEEE Transactions on Neural Networks information for authors.

Subjects
MANUSCRIPTS, COPYRIGHT, INTELLECTUAL property, ELECTRONIC journals, AUTHORS
Abstract

The article presents informations for the authors of the journal. Topics discussed include the style for manuscript, open access, and page charges. Also being discussed are the rules regarding copyright and copying, submission of final manuscript and electronic version, and manuscripts and electronic file.

Published
2015
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35. IEEE Transactions on Neural Networks information for authors.

Subjects
ARTIFICIAL neural networks, PERIODICALS, MANUSCRIPTS, AUTHORS
Abstract

This section provides information for authors of "IEEE Transactions on Neural Networks and Learning Systems," including types of contributions, submission of manuscripts, and professional editing services.

Published
2015
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41. Organizing Books and Authors by Multilayer SOM.

Author

Zhang, Haijun, Chow, Tommy W. S., and Wu, Q. M. Jonathan

Subjects
SELF-organizing maps, ELECTRONIC books, AUTHORS
Abstract

This paper introduces a new framework for the organization of electronic books (e-books) and their corresponding authors using a multilayer self-organizing map (MLSOM). An author is modeled by a rich tree-structured representation, and an MLSOM-based system is used as an efficient solution to the organizational problem of structured data. The tree-structured representation formulates author features in a hierarchy of author biography, books, pages, and paragraphs. To efficiently tackle the tree-structured representation, we used an MLSOM algorithm that serves as a clustering technique to handle e-books and their corresponding authors. A book and author recommender system is then implemented using the proposed framework. The effectiveness of our approach was examined in a large-scale data set containing 3868 authors along with the 10500 e-books that they wrote. We also provided visualization results of MLSOM for revealing the relevance patterns hidden from presented author clusters. The experimental results corroborate that the proposed method outperforms other content-based models (e.g., rate adapting poisson, latent Dirichlet allocation, probabilistic latent semantic indexing, and so on) and offers a promising solution to book recommendation, author recommendation, and visualization. [ABSTRACT FROM PUBLISHER]

Published
2016
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