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首页> 外文会议>16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003 Aug 24-27, 2003 Washington, DC, USA >Sparse Probability Regression by Label Partitioning
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Sparse Probability Regression by Label Partitioning

机译：标签划分的稀疏概率回归

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摘要

A large-margin learning machine for sparse probability regression is presented. Unlike support vector machines and other forms of kernel machines, nonlinear features are obtained by transforming labels into higher-dimensional label space rather than transforming data vectors into feature space. Linear multi-class logistic regression with partitioned classes of labels yields a nonlinear classifier in the original labels. With a linear kernel in data space, storage and run-time requirements are reduced from the number of support vectors to the number of partitioned labels. Using the partitioning property of KL-divergence in label space, an iterative alignment procedure produces sparse training coefficients. Experiments show that label partitioning is effective in modeling nonlinear decision boundaries with same, and in some cases superior, generalization performance to Support Vector Machines with significantly reduced memory and run-time requirements.
机译：提出了一种用于稀疏概率回归的大利润学习机。与支持向量机和其他形式的内核机不同，非线性特征是通过将标签转换为高维标签空间而不是将数据向量转换为特征空间来获得的。带有标签分类类别的线性多类逻辑回归在原始标签中产生非线性分类器。使用数据空间中的线性内核，存储和运行时要求从支持向量的数量减少到分区标签的数量。利用标签空间中KL散度的划分特性，迭代对齐过程将产生稀疏的训练系数。实验表明，标签划分可有效地对非线性决策边界建模，并且在某些情况下优于支持向量机（在某些情况下具有更好的泛化性能），并且大大减少了内存和运行时间要求。

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