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Support vector machine (SVM) algorithm has shown a good learning ability and generalization ability in classification, regression and forecasting. This paper mainly analyzes the the performance of support vector machine algorithm in the classification problem, including the algorithm in the kernel function selection, parameter optimization, and integration of other algorithms and to deal with multi-classification...
A novel support vector machine (SVM) algorithm for regression problems is proposed in this paper. Each pattern in the original training set is converted into a pair of patterns, which are labeled by 1 and -1, respectively. Therefore, the regression problem can be considered as a classification problem. By optimizing the obtained decision function, the model output of unknown samples can be estimated...
For classification problem clustering method divides the dataset into many clusters based on correlation attribute of all elements of the dataset. How to classify data within the same clustering number as close as possible and data in different clusters as depart as possible is the key of clustering method. Clustering support vector machines (ClusterSVM) partition the training data into disjoint clusters...
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