C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Machine Learning
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Gallbladder boundary segmentation from ultrasound images using active contour model
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Gallbladder segmentation in 2-D ultrasound images using deformable contour methods
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
SVM approach to classifying lesions in USG images with the use of the gabor decomposition
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Hi-index | 0.00 |
This article presents the use of Support Vector Machines (SVM) to diagnose the ischemic heart disease using heart images obtained from Single Proton Emission Computed Tomography (SPECT). The data set came from 267 different patients and was divided into several sub-sets containing training and validation data. The study consisted in comparing results of classifying cardiac SPECT images using SVMs with those obtained using another method of machine learning CLIP3 which is a combination of the decision tree algorithm and the rule induction algorithm. Validations carried out using a SPECT image database have shown that SVMs are good in generalising knowledge gained about multi-dimensional data with relatively little training data.