Exact Convex Confidence-Weighted Learning
Exact Convex Confidence-Weighted Learning
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发表时间：
2008-12
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通讯作者：
K. Crammer;Mark Dredze;Fernando C Pereira
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作者：
K. Crammer;Mark Dredze;Fernando C Pereira
置信加权（CW）学习[6]是一种线性分类器的在线学习方法，它在权重向量上保持高斯分布，并使用表示权重和相关性不确定性的协方差矩阵。置信约束确保从假设分布中提取的权重向量能够以指定的概率正确地对示例进行分类。在此框架内，我们推导了约束的新凸形式，并在错误界限模型中对其进行了分析。对合成数据和文本数据的实证评估表明，我们的 CW 学习版本比常用的一阶和二阶在线方法实现了更低的累积误差和样本外误差。
Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that represents uncertainty about weights and correlations. Confidence constraints ensure that a weight vector drawn from the hypothesis distribution correctly classifies examples with a specified probability. Within this framework, we derive a new convex form of the constraint and analyze it in the mistake bound model. Empirical evaluation with both synthetic and text data shows our version of CW learning achieves lower cumulative and out-of-sample errors than commonly used first-order and second-order online methods.