Article
Refine
Year of publication
- 2003 (2) (remove)
Document Type
- Article (2) (remove)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- Kongress (1)
- Online-Publikation (1)
- Preßburg <2003> (1)
- Theoretische Informatik (1)
Institute
- Informatik (2) (remove)
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2003
We investigate unary regular languages and compare deterministic finite automata (DFA’s), nondeterministic finite automata (NFA’s) and probabilistic finite automata (PFA’s) with respect to their size. Given a unary PFA with n states and an e-isolated cutpoint, we show that the minimal equivalent DFA has at most n exp 1/2e states in its cycle. This result is almost optimal, since for any alpha < 1 a family of PFA’s can be constructed such that every equivalent DFA has at least n exp alpha/2e states. Thus we show that for the model of probabilistic automata with a constant error bound, there is only a polynomial blowup for cyclic languages. Given a unary NFA with n states, we show that efficiently approximating the size of a minimal equivalent NFA within the factor sqrt(n)/ln n is impossible unless P = NP. This result even holds under the promise that the accepted language is cyclic. On the other hand we show that we can approximate a minimal NFA within the factor ln n, if we are given a cyclic unary n-state DFA.