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We present an improved online learning algorithm for sparse kernel partial least squares, this algorithm improves current methods to kernel-based regression in two aspects. First, it operates online at each time step when it acquires a new input support vector, performs an update and drop out the old data to adapted process changes. Second, it effectively reduces the dimension of feature space and...
Machine learning algorithms for large scale data are becoming more crucial in today's world. This is due to the unprecedented size of streaming data being collected by information technology. Incremental learning is considered one of the key concepts for learning from streaming data where a learned model is updated when new data becomes available in time. In this paper, we study RBF-SVM local incremental...
Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on nu-transductive support vector machines for classification (nu-TSVC) and construct a new algorithm - Unconstrained nu-Transductive Support Vector Machines (Unu-TSVM). After researching on the special construction of primal problem in nu-TSVM, we transform...
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