Specific-to-general learning for temporal events
Specific-to-general learning for temporal events
复制标题
针对时间事件的具体到一般的学习
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
J. Siskind
中科院分区：
文献类型：
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作者：
Alan Fern;R. Givan;J. Siskind
在一种简单的时态事件描述语言中，研究了事件类的监督学习问题。我们给出了这一逻辑的两个表达能力强大的子集的包含和泛化问题的上下界和算法，并在此基础上给出了一种仅有正例的具体到一般的学习方法。我们还提出了一个多项式时间可计算的“句法”包含测试，它意味着语义包含而不是等价的。可以使用基于句法包含的泛化算法来代替语义泛化，以提高所得到的学习算法的渐近复杂性。一篇配套的论文表明，我们的方法可以用于在视频事件识别的实质性应用领域复制人类编码概念的性能。
We study the problem of supervised learning of event classes in a simple temporal event-description language. We give lower and upper bounds and algorithms for the subsumption and generalization problems for two expressively powerful subsets of this logic, and present a positive-examples-only specific-to-general learning method based on the resulting algorithms. We also present a polynomial-time computable "syntactic" subsumption test that implies semantic subsumption without being equivalent to it. A generalization algorithm based on syntactic subsumption can be used in place of semantic generalization to improve the asymptotic complexity of the resulting learning algorithm. A companion paper shows that our methods can be applied to duplicate the performance of human-coded concepts in the substantial application domain of video event recognition.