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Content Provider | IEEE Xplore Digital Library |
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Author | Marzi, A. Marzi, H. |
Copyright Year | 2015 |
Description | Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Univ. of Ottawa, Ottawa, ON, Canada (Marzi, A.) || Dept. of Inf. Syst., St. Francis Xavier Univ., Antigonish, NS, Canada (Marzi, H.) |
Abstract | The objective was to train several Artificial Neural Networks (ANNs) with different training functions in order to gain an understanding of the effect of dataset complexity on performance. The utilization of varying training functions permitted ANN diversity; and allowing for enhanced diagnostic reasoning in classification. This improvement is achieved by expediting training stage, calibrating classification. The proposed technique is applied to a number of dataset to verify performance improvements. Particular application of the proposed technique is demonstrated by applying methodology for medical diagnostics. |
Starting Page | 1 |
Ending Page | 4 |
File Size | 727249 |
Page Count | 4 |
File Format | |
ISBN | 9781479989614 |
e-ISBN | 9781479989638 |
DOI | 10.1109/IHTC.2015.7238069 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-05-31 |
Publisher Place | Canada |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Training Diagnostics Artificial neural networks Classification Neural Networks Breast cancer Classification algorithms Artificial intelligence Medical diagnostic imaging Diseases |
Content Type | Text |
Resource Type | Article |
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