The training time for Support vector machine (SVM) depends largely on the size of the
training set, which makes it impractical for large data sets. This paper presents a new …

Training support vector machines involves a huge optimization problem and many specially
designed algorithms have been proposed. In this paper, we proposed an algorithm called …

A serious problem of support vector machine (SVM) is its low classifying speed. The speed
depends on the number of support vectors. The clustering SVM, proposed in this paper, is a …

In a standard support vector machine (SVM), the training process has O (n 3) time and O (n
2) space complexities, where n is the size of training dataset. Thus, it is computationally …

Support vector machines are currently very popular approaches to supervised learning.
Unfortunately, the computational load for training and classification procedures increases …

This paper proposes two methods which take advantage of k-mean clustering algorithm to
decrease the number of support vectors (SVs) for the training of support vector machine …

Background This paper proposes a learning strategy of SVM used to large training set. First
authors train an initial classifier with a small training set, then prune the large training set …

A novel algorithm which combines clustering analysis and SVM is proposed for
classification. Specifically, based on the conglomeration and decentralization characteristics …

Support Vector Machine (SVM) training in a large data set involves a huge optimization
problem to make SVM impractical even for a moderate data set. In this paper, we propose a …

Support vector machines (SVM) are widely applied to various classification problems.
However, most SVM need lengthy computation time when faced with a large and …