| 摘要: |
| 在大规模或实时环境要求下,机器学习算法的计算效率非常重要.描述了用于最大熵模型执行系统的一种高效的数据结构及其相关的生成和查找算法.这种数据结构称为稀疏特征树,用于表示特征集合,以提高特征查找(或特征匹配)的速度,从而提高概率计算和执行系统的速度.基本短语识别和词性标注的实验显示,这种新的数据结构的确能够极大地加快最大熵方法执行系统的速度,同时保持空间复杂度不变. |
| 关键词: 稀疏特征树 最大熵模型 特征匹配 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60435020 (国家自然科学基金) |
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| A Feature Matching Method: Sparse Feature Tree |
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ZHOU Ya-Qian,HUANG Xuan-Jing,WU Li-De
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| Abstract: |
| Computational efficiency is an important concern for machine learning algorithms, especially for applications on large test sets or in real-time scenarios. In this paper, a novel data structure and the corresponding algorithms for the execution system of the maximum entropy model are described. This data structure, called sparse feature tree, is used to represent the feature set to speed up the process of feature search (or feature matching), so that speed up the process of probability calculation and execution system. Experiments on chunking recognition and Part-of-Speech tagging are conducted to show that the new data structure greatly speeds up the feature matching process while keeping the same space complexity. |
| Key words: sparse feature tree maximum entropy model feature matching |
