The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Training Invariant Support Vector Machines
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension
IEEE Transactions on Knowledge and Data Engineering
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Use of Support Vector Machines and Neural Network in Diagnosis of Neuromuscular Disorders
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Breast cancer diagnosis using least square support vector machine
Digital Signal Processing
Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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Machine learning techniques have gained increasing demand in biomedical research due to capability of extracting complex relationships and correlations among members of the large data sets. Thus, over the past few decades, scientists have been concerned about computer information technology to provide computational learning methods for solving the complex medical problems. Support Vector Machine is an efficient classifier that is widely applied to biomedical and other disciplines. In recent years, new opportunities have been developed on improving Support Vector Machines' classification efficiency by combining with any other statistical and computational methods. This study proposes a new method of Support Vector Machines for influential classification using combined kernel functions. The classification performance of the developed method, which is a type of non-linear classifier, was compared to the standart Support Vector Machine method by applying on seven different datasets of medical diseases. The results show that the new method provides a significant improvement in terms of the probability excess.