Implicant network: an associative memory model

被引：0

作者：

Federici, D ^{[1
]}

机构：

[1] Norwegian Univ Sci & Technol, Dept Comp & Informat Sci, N-7491 Trondheim, Norway

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Implicant Network is a neural network model capable of storing an arbitrary boolean function F : {0, 1}(n) --> {0, 1}. The difference from previous one-shot learning models is that the training algorithm compresses the positive set online with linear time and space requirements. The algorithm works by building a Sum Of Products (SOP) representation of the positive set as it is presented to the network. Since the minimum coverage of implicants is an NP-Hard problem, the compression rate is not optimal at first but it is shown to increase rapidly as the positive set is shown over again.

引用

页码：3036 / 3041

页数：6

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