By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- Current study results on Machine Learning - Support Vector Machines have been published. According to news originating from Hangzhou, People’s Republic of China, by VerticalNews correspondents, research stated, “Recently, support vector machine (SVM) has received much attention due to its good performance and wide applicability. As a supervised learning algorithm, the standard SVM uses sufficient labeled data to obtain the optimal decision hyperplane.”
Financial supporters for this research include National Natural Science Foundation of China, Natural Science Foundation.
Our news journalists obtained a quote from the research from Zhejiang University, “However, in many practical applications, it is difficult and/or expensive to obtain labeled data. Besides, the standard SVM is a batch learning algorithm. It is inefficient to handle streaming data as the classifier must be retrained from scratch whenever a new data is arrived. In this paper, we consider the online classification of streaming data when only a small portion of data are labeled while a large portion of data are unlabeled. In order to obtain an adaptive solution with relatively low computational complexity, a new form of manifold regularization is proposed. Then, an adaptive and online semi-supervised least square SVM is developed, which well exploits the information of new incoming labeled or unlabeled data to boost learning performance.”
According to the news editors, the research concluded: “Simulations on synthetic and real data sets show that the proposed algorithm achieves good classification performance even if there only exist a few labeled data.”
For more information on this research see: Online semi-supervised support vector machine. Information Sciences , 2018;439():125-141. Information Sciences can be contacted at: Elsevier Science Inc, 360 Park Ave South, New York, NY 10010-1710, USA. (Elsevier - www.elsevier.com; Information Sciences - http://www.journals.elsevier.com/information-sciences/)
The news correspondents report that additional information may be obtained from Y. Liu, Zhejiang University, Coll Informat Sci & Elect Engn, Hangzhou 310027, Zhejiang, People’s Republic of China. Additional authors for this research include Z. Xu and C.G. Li.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.ins.2018.01.048. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-23), Data from Zhejiang University Provide New Insights into Support Vector Machines (Online semi-supervised support vector machine), Journal of Robotics & Machine Learning, 43, ISSN: 1944-186X, BUTTER® ID: 015547893
From the newsletter Journal of Robotics & Machine Learning.
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