Inducing features of random fields
Inducing features of random fields
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DOI：
10.1109/34.588021
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发表时间：
1997-04-01
影响因子：
23.6
通讯作者：
Lafferty, J
中科院分区：
文献类型：
--
作者：
DellaPietra, S;DellaPietra, V;Lafferty, J
我们提出了一种从训练样本中构造随机场的技术。学习范式通过允许由越来越大的子图支持的潜在功能或特征来构建越来越复杂的领域。每个特征都有一个权重，通过最小化模型和训练数据的经验分布之间的Kuliback-Leibler散度来训练。贪婪算法决定如何将特征增量添加到字段中，迭代缩放算法用于估计权重的最优值。本文介绍的随机场模型和技术与大多数计算机视觉文献中常见的随机场模型和技术不同，因为底层随机场是非马尔可夫的，并且有大量必须估计的参数。给出了与其他学习方法的关系，包括决策树。作为对该方法的演示，我们描述了该方法在自然语言处理中的自动词分类问题中的应用。
We present a technique for constructing random fields from a see of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kuliback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Relations to other learning approaches, including decision trees, are given. As a demonstration of the method, we describe its application to the problem of automatic word classification in natural language processing.