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Normal SVM is not suitable for classification problems on large data sets because of high training complexity. To build a distributed learning framework and apply cooperative learning strategy with multiple SVM classifiers are the good inspirations to data stream mining. In this paper, a SVMs' cooperative learning strategy based on multiple agent system is proposed according to cooperative and distributional...
Branch and bound for semi-supervised support vector machines as an exact, globally optimal solution is useful for benchmarking different practical S3VM implementation. But, global optimization can be computationally very demanding. Parallel implementation of the algorithm enables us to reduce computational time significantly and to solve larger problems. Focusing on the time consuming problem of BBS3VM,...
Incremental SVM framework is often designed to deal with large-scale learning and classification problems. The paper presents a new dynamic incremental learning algorithm for mining data streams. The multiple classifiers are constructed according to the statistic characters of batched training data in data streams. The feature space of all data is partitioned according to the performance of each classifier...
Support vector machine (SVM) has become a popular classification tool but the main disadvantages of SVM algorithms are their large memory requirement and computation time to deal with very large datasets. To speed up the process of training SVM, parallel methods have been proposed by splitting the problem into smaller subsets and training a network to assign samples of different subsets. A parallel...
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