CRAB 发表于 2025-3-25 08:34:21
The role of prototypicality in exemplar-based learning,perties approach, and a similarity-based approach, and suggests measures that implement the different approaches. The proposed measures are tested in a set of experiments. The results of the experiments show that prototypicality serves as a good storing filter in storage reduction algorithms; combinFriction 发表于 2025-3-25 13:45:00
Specialization of recursive predicates,sible to specialize or remove any of the clauses in a refutation of a negative example without excluding any positive examples. A previously proposed solution to this problem is to apply program transformation in order to obtain non-recursive target predicates from recursive ones. However, the appliBROTH 发表于 2025-3-25 18:29:38
http://reply.papertrans.cn/63/6208/620758/620758_24.png暴露他抗议 发表于 2025-3-25 22:48:24
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http://reply.papertrans.cn/63/6208/620758/620758_26.pngQUAIL 发表于 2025-3-26 06:33:08
http://reply.papertrans.cn/63/6208/620758/620758_27.png不可磨灭 发表于 2025-3-26 11:32:09
http://reply.papertrans.cn/63/6208/620758/620758_28.pngcomely 发表于 2025-3-26 14:11:32
The power of decision tables,spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance iProstatism 发表于 2025-3-26 19:24:44
Pruning multivariate decision trees by hyperplane merging,y contain binary tests questioning to what side of a hyperplane the example lies. Most of these algorithms use . mechanisms similar to those of traditional decision trees. Nearly unexplored remains the large domain of . methods, where a new decision test (derived from previous decision tests) replacextemporaneous 发表于 2025-3-26 21:14:38
Multiple-Knowledge Representations in concept learning,nown that biases used in learning algorithms directly affect their performance as well as their comprehensibility. A critical problem is that, most of the time, the most “comprehensible” representations are not the best performer in terms of classification! In this paper, we argue that concept learn