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首页> 外文期刊>IEEE transactions on systems, man, and cybernetics. Part B >Designing syntactic pattern classifiers using vector quantization and parametric string editing
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Designing syntactic pattern classifiers using vector quantization and parametric string editing

机译:使用矢量量化和参数字符串编辑设计句法模式分类器

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摘要

We consider a fundamental inference problem in syntactic pattern recognition (PR). We assume that the system has a dictionary which is a collection of all the ideal representations of the objects in question. To recognize a noisy sample, the system compares it with every element in the dictionary based on a nearest-neighbor philosophy, using three standard edit operations: substitution, insertion, and deletion, and the associated primitive elementary edit distances d(.,.). In this paper, we consider the assignment of the inter-symbol distances using the parametric distances. We show how the classifier can be trained to get the optimal parametric distance using vector quantization in the meta-space. In all our experiments, the training was typically achieved in a very few iterations. The subsequent classification accuracy we obtained using this single-parameter scheme was 96.13%. The power of the scheme is evident if we compare it to 96.67%, which is the accuracy of the scheme which uses the complete array of inter-symbol distances derived from a knowledge of all the confusion probabilities.
机译:我们考虑句法模式识别(PR)中的一个基本推理问题。我们假设系统有一个字典,该字典是所讨论对象的所有理想表示形式的集合。为了识别嘈杂的样本,系统会基于最近邻居的原理,使用三种标准的编辑操作(替换,插入和删除)以及相关的原始基本编辑距离d(。,。),将其与字典中的每个元素进行比较。 。在本文中,我们考虑使用参数距离分配符号间距离。我们展示了如何使用元空间中的矢量量化训练分类器以获得最佳参数距离。在我们所有的实验中,训练通常都是在很少的迭代中完成的。使用该单参数方案获得的后续分类精度为96.13%。如果将其与96.67%进行比较,则该方案的功效显而易见,这是该方案的准确性,该方案使用了从所有混淆概率知识中得出的完整符号间距离数组。

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