The SVM based Recursive Feature Elimination (RFE-SVM) algorithm is a popular technique for feature selection, used in natural language processing and bioinformatics. Recently it was demonstrated that a small regularisation constant C can considerably improve the performance of RFE-SVM on microarray datasets. In this paper we show that further improvements are possible if the explicitly computab...

MRI is the most important technique, in detecting the brain tumor. In this paper data mining methods are used for classification of MRI images. A new hybrid technique based on the support vector machine (SVM) and fuzzy c-means for brain tumor classification is proposed. The purposed algorithm is a combination of support vector machine (SVM) and fuzzy c-means, a hybrid technique for prediction o...

Abstract In order to solve the problem of low accuracy in oil-gas pipeline leak detection, a detection method based on Particle Swarm Optimization (PSO) algorithm optimized Support Vector Machine (SVM) is introduced. This uses PSO penalty factor ‘c’ and kernel function parameter ‘g’, constructs leakage model SVM. We set up an experimental platform collect negative pressure wave signals under di...

In recent years,machine learning method has been applied to the extensive research on traffic classification. In these methods, SVM (Support vector machine) is a supervised learning which can improve generalization ability of learning machine effectively. However, the penalty parameter C and kernel function parameter  are generally given by test experience during training of SVM. How to determ...

A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious a large advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields. To some degree, the optimization of SVM depends on its kernel function and Slack variable, the determinant of which is its parameters δ and c in the classification functi...

Data Mining has great scope in the field of medicine. In this article we introduced one new fuzzy approach for prediction of hepatitis disease. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for reprocessing and then using KNN for classification. In our approach Naive Bayes c...

We present a stream algorithm for large scale classification (in the context of l2-SVM) by leveraging connections between learning and computational geometry. The stream model [1] imposes the constraint that only a single pass over the data is allowed. We study the streaming model for the problem of binary classification with SVMs and propose a single pass SVM algorithm based on the minimum enc...