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DOI: 10.14569/IJACSA.2017.080755
PDF

New Deep Kernel Learning based Models for Image Classification

Author 1: Rabha O. Abd-elsalam
Author 2: Yasser F.Hassan
Author 3: Mohamed W.Saleh

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 7, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Deep learning system is used for solving many problems in different domains but it gives an over-fitting risk when richer representations are increased. In this paper, three different models with different deep multiple kernel learning architectures are proposed and evaluated for the breast cancer classification problem. Discrete Wavelet transform and edge histogram descriptor are used to extract the image features. For image classification purpose, support vector machine with the proposed deep multiple kernel models are used. Also, the span bound is employed for optimizing these models over the dual objective function. Furthermore, the comparison between the performance of the traditional support vector machine which uses only single kernel and the introduced models is worked out that show the efficiency of the experimental results of the proposed models.

Keywords: Deep learning; multiple kernel; support vector machine; image classification

Rabha O. Abd-elsalam, Yasser F.Hassan and Mohamed W.Saleh, “New Deep Kernel Learning based Models for Image Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 8(7), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080755

@article{Abd-elsalam2017,
title = {New Deep Kernel Learning based Models for Image Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080755},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080755},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {7},
author = {Rabha O. Abd-elsalam and Yasser F.Hassan and Mohamed W.Saleh}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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