Breast cancer detection from FNA using SVM with different parameter tuning systems and SOM-RBF classifier

DOI10.1016/j.jfranklin.2006.09.005zbMath1115.92033OpenAlexW2036271857MaRDI QIDQ2642220

Asoke K. Nandi, Tingting Mu

Publication date: 20 August 2007

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2006.09.005

zbMATH Keywords

radial basis function networkssupport vector machinesself-organizing mapsparameter tuningbreast cancer detection

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biomedical imaging and signal processing (92C55) Medical applications (general) (92C50) Stochastic learning and adaptive control (93E35)

Related Items (1)

Calibration of \(\epsilon\)-insensitive loss in support vector machines regression

Uses Software

• SVM

Cites Work

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• Learning good prototypes for classification using filtering and abstraction of instances
• Support-vector networks
• Radius Margin Bounds for Support Vector Machines with the RBF Kernel
• Breast Cancer Diagnosis and Prognosis Via Linear Programming
• Choosing multiple parameters for support vector machines

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