Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso

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• 关键词：SVMs ; Kernel methods ; Sparse approximation ; Lasso
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10125
• 期：1
• 页码：208-216
• 丛书名：Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
• ISBN：978-3-319-52277-7
• 卷排序：10125

文摘

Performing predictions using a non-linear support vector machine (SVM) can be too expensive in some large-scale scenarios. In the non-linear case, the complexity of storing and using the classifier is determined by the number of support vectors, which is often a significant fraction of the training data. This is a major limitation in applications where the model needs to be evaluated many times to accomplish a task, such as those arising in computer vision and web search ranking.