StructBoost：预测结构化输出变量的Boosting方法
StructBoost: Boosting Methods for Predicting Structured Output Variables
摘要：
Boosting是一种通过线性组合一组不太准确的弱学习器来学习单个准确预测器的方法。近年来，结构化学习在计算机视觉中得到了广泛的应用。受结构化支持向量机（SSVM）的启发，本文提出了一种用于结构化输出预测的新的boosting算法，称为StructBoost。StructBoost通过组合一组弱结构化学习器来支持非线性结构化学习。由于SSVM推广了SVM，我们的StructBoost将标准的提升方法（如AdaBoost或LPBoost）推广到结构化学习。StructBoost的优化问题比SSVM更具挑战性，因为它可能涉及指数级的许多变量和约束。相比之下，对于SSVM，通常具有指数数量的约束，并且使用切割平面方法。为了有效地解决StructBoost，我们制定了一个等效的$ 1 $ -松弛公式，并使用切割平面和列生成的组合来解决它。我们展示了StructBoost在一系列问题上的多功能性和实用性，例如优化分层多类分类的树损失，优化Pascal重叠标准以实现强大的视觉跟踪和学习条件随机场参数以进行图像分割。
英文摘要：
Boosting is a method for learning a single accurate predictor by linearly combining a set of less accurate weak learners. Recently, structured learning has found many applications in computer vision. Inspired by structured support vector machines (SSVM), here we propose a new boosting algorithm for structured output prediction, which we refer to as StructBoost. StructBoost supports nonlinear structured learning by combining a set of weak structured learners. As SSVM generalizes SVM, our StructBoost generalizes standard boosting approaches such as AdaBoost, or LPBoost to structured learning. The resulting optimization problem of StructBoost is more challenging than SSVM in the sense that it may involve exponentially many variables and constraints. In contrast, for SSVM one usually has an exponential number of constraints and a cutting-plane method is used. In order to efficiently solve StructBoost, we formulate an equivalent $ 1 $-slack formulation and solve it using a combination of cutting planes and column generation. We show the versatility and usefulness of StructBoost on a range of problems such as optimizing the tree loss for hierarchical multi-class classification, optimizing the Pascal overlap criterion for robust visual tracking and learning conditional random field parameters for image segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
