A Hybrid Approach to Breast Cancer Diagnosis-Reference-Cited by-同舟云学术

A Hybrid Approach to Breast Cancer Diagnosis

Published:2001 Issue: Volume: Page:299-330
ISSN:1382-3434
Container-title:Practical Applications of Computational Intelligence Techniques
language:
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Author:

Sordo M.,Buxton H.,Watson D.

Publisher

Springer Netherlands

Link

http://link.springer.com/content/pdf/10.1007/978-94-010-0678-1_10

Reference40 articles.

1. Towell, G.G. (1991)Symbolic Knowledge and Neural Networks: Insertion Refinement and ExtractionPh.D. thesis, University of Wisconsin, Madison.

2. Sordo, M., Buxton, H., Watson, D., Collins, D., Ronen, S., Leach, M., and Payne, G. (1998), “KBANNs for classification of normal31P MRS based on hormonedependent changes during the menstrual cycle,”Fourth International Conference on Neural Networks and their ApplicationsMarseilles, France.

3. Sordo, M., Buxton, H., and Watson, D. (1999), “KBANNs and the classification of3 tP MRS of malignant mammary tissues,”Ninth International Conference on Artificial Neural NetworksEdinburgh, U.K.

4. Baxt, W.G. (1995), “Application of artificial neural networks to clinical medicine,”Lancetvol. 346, pp. 1135–1138.

5. Sordo, M. (1999)A Neurosymbolic Approach to the Classification of Scarce and Complex DataD.Phil. thesis, University of Sussex, Brighton, U.K.

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