Improving Hash Table Hit Ratio of an ILP-Based Concept Discovery System with Memoization Capabilities-Reference-Cited by-同舟云学术

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Improving Hash Table Hit Ratio of an ILP-Based Concept Discovery System with Memoization Capabilities

Published:2012-10-30 Issue: Volume: Page:261-269
ISSN:
Container-title:Computer and Information Sciences III
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Author:

Mutlu Alev,Senkul Pinar

Publisher

Springer London

Link

http://link.springer.com/content/pdf/10.1007/978-1-4471-4594-3_27.pdf

Reference17 articles.

1. Muggleton, S.: Inductive logic programming. In: Wilson, R.A., Keil, F.C. (eds.) The MIT Encyclopedia of the Cognitive Sciences (MITECS). MIT Press, Cambridge (1999)

2. Dzeroski, S.: Multi-relational data mining: an introduction. SIGKDD Explor. 5(1), 1–16 (2003)

3. Bratko, I., King, R.D.: Applications of inductive logic programming. SIGART Bull. 5(1), 43–49 (1994)

4. Blockeel, H., Dehaspe, L., Demoen, B., Janssens, G., Vandecasteele, H.: Improving the efficiency of inductive logic programming through the use of query packs. J. Artif. Intell. Res. 16, 135–166 (2002)

5. Costa, V.S., Srinivasan, A., Camacho, R., Blockeel, H., Demoen, B., Janssens, G., Struyf, J., Vandecasteele, H., Laer, W.V.: Query transformations for improving the efficiency of ILP systems. J. Mach. Learn. Res. 4, 465–491 (2003)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Cosine Similarity-Based Pruning for Concept Discovery;Communications in Computer and Information Science;2016

2. Utilizing Coverage Lists as a Pruning Mechanism for Concept Discovery;Information Sciences and Systems 2014;2014

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