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Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
SVM has been used in speaker identification successfully, whereas training SVM consumes long computing time and large memory with all training data, therefore the training data selection (TDS) is an important step for effective speaker identification system. In this paper, a novel TDS method based on the PCA and improved ant colony cluster (IACC) is proposed to solve this problem existed in SVM. The...
In order to improve the training efficiency to the data set, an improved adaptive Support Vector Machine (SVM) algorithm with combinational Fuzzy C-means Clustering is proposed. With multi-layer fuzzy C-means clustering algorithm original data are pretreated to remove the training data, which has no contribution to the classification. The remaining data are used to complete the training work for SVM...
We consider the problem of SVM batch active learning, which involves distinguishing samples chosen and maximum approximate the real normal vector w in feature space. Although several studies are devoted to batch mode active learning, they suffer either from the uncertain parameter set or from the solutions of local optimization. We introduce a new algorithm for performing batch active learning by...
Ant colony optimization (ACO) is a kind of bionic swarm intelligence algorithm belongs to artificial intelligence (AI) field and has been successfully applied in resolving complex optimization problems. Support vector machine (SVM) is a new machine learning method with greater generalization performance, and has shown its superiority in classification and regression problems. By combining the advantages...
Maximum margin clustering (MMC), which extends the theory of support vector machine to unsupervised learning, has been attracting considerable attention recently. The existing approaches mainly focus on reducing the computational complexity of MMC. The accuracy of these methods, however, has not always been guaranteed. In this paper, we propose to incorporate additional side-information, which is...
Microarray technology facilitates the monitoring of the expression profile of a large number of genes across different experimental conditions simultaneously. This article proposes a novel approach that combines a recently proposed multiobjective fuzzy clustering scheme with support vector machine (SVM), to yield improved solutions. The multiobjective technique is first used to produce a set of non-dominated...
This paper presents two new types of support vector machine (SVM) algorithms, one is based on Hard-margin SVM and the other is based on Soft-margin SVM. These algorithms can handle data with tolerance of which the concept includes some errors, ranges or missing values in data. First, the concept of tolerance is introduced into optimization problems of Support Vector Machine. Second, the optimization...
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