When Does Greedy Learning of Relevant Attributes Succeed?

Arpe, Jan; Reischuk, Rüdiger

doi:10.1007/978-3-540-73545-8_30

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Computational Complexity Issue1

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“…Though it is quite difficult to reduce the worst case time complexity, some greedy type approximation algorithms have been proposed. It is proven under the uniform distribution of samples that greedy type algorithms can identify BNs with high probability for wide-class of Boolean functions [6,12]. Furthermore, some sophisticated algorithms are proposed which work for all types of Boolean functions under the uniform distribution [23].…”

Section: Computational Complexity Issuementioning

confidence: 99%

Algorithms for Inference, Analysis and Control of Boolean Networks

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Boolean networks (BNs) are known as a mathematical model of genetic networks. In this paper, we overview algorithmic aspects of inference, analysis and control of BNs while focusing on the authors' works. For inference of BN, we review results on the sample complexity required to uniquely identify a BN. For analysis of BN, we review efficient algorithms for identifying singleton attractors. For control of BN, we review NP-hardness results and dynamic programming algorithms for general and special cases.

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Section: Computational Complexity Issuementioning

confidence: 99%