Making large scale SVM learning practical
Making large scale SVM learning practical
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DOI：
10.17877/de290r-14262
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发表时间：
1998
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影响因子：
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通讯作者：
T. Joachims
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文献类型：
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作者：
T. Joachims
训练支持向量机SVM导致具有边界约束和一个线性等式约束的二次优化问题。尽管这种类型的问题已经很好地理解了，但在设计SVM学习器时需要考虑许多问题。特别是，对于具有许多现成训练示例的大型学习任务，一般二次规划的优化技术在其存储器和时间要求方面很快变得难以处理。SVM light是SVM学习器的一种实现，它解决了大型任务的问题。本章介绍了为SVM light V2.0开发的算法和计算结果，这使得大规模SVM训练更加实用。研究结果为支持向量机在大领域的应用提供了指导。
Training a support vector machine SVM leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples on the shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVM light is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic and computational results developed for SVM light V 2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains.