学习 = 表示 + 评价 + 优化 表示(Representation) : 学习器的假设空间(hypothesis space) 评价(Evaluation) : 评价函数用于判 首页 上一页 1 下一页 尾页...
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With the inductive program synthesis system NDIPS, this paper presents its synthesis algorithm, the organization of its hypothesis space and the search strategies it adopted.
本文以归纳程序综合系统NDIPS为背景,介绍自顶向下的归纳程序综合算法,以及程序假设空间的组织、搜索所采用的技术和策略。
The two algorithms for concept learning efficiently complete the whole search through the hypothesis space based on a very useful structure: the general-to-specific partial order.
对于这两种算法来说,都是利用一般到特殊序的偏序结构来完成整个搜索过程。
The classification results of basis classifiers were added to original feature vector to obtain the meta-level data, which enhanced the search ability in hypothesis space and decreased system bias.
通过扩展特征向量,元学习增强了对假设空间的表达能力,降低了系统的偏差。
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