Cutting-Plane Methods in Machine Learning
Cutting-Plane Methods in Machine Learning
复制标题
机器学习中的剖切面方法
DOI：
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发表时间：
2013
期刊：
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通讯作者：
Vojtech Franc
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文献类型：
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作者：
Vojtech Franc
切削平面方法是优化技术，通过线性不平等，称为切割平面的线性不平等，逐步构建可行集合的近似值或目标函数。这个基本思想的许多变体都是凸非平滑优化和整数线性编程中使用的标准工具之一。最近，切好的平面方法对机器学习领域的兴趣越来越大。在本章中，我们描述了这些方法背后的基本理论，并展示了它们在解决机器学习问题上的三个成功应用：正则风险最小化，多个内核学习和图形模型中的MAP推断。
Cutting-plane methods are optimization techniques that incrementally construct an approximation of a feasible set or an objective function by linear inequalities called cutting planes. Numerous variants of this basic idea are among standard tools used in convex nonsmooth optimization and integer linear programing. Recently, cutting-plane methods have seen growing interest in the field of machine learning. In this chapter, we describe the basic theory behind these methods and show three of their successful applications to solving machine learning problems: regularized risk minimization, multiple kernel learning, and MAP inference in graphical models.