Advances in Neural Networks – ISNN 2011: 8th International by Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang

By Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)

The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed court cases of the eighth foreign Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The overall of 215 papers offered in all 3 volumes have been rigorously reviewed and chosen from 651 submissions. The contributions are established in topical sections on computational neuroscience and cognitive technological know-how; neurodynamics and complicated structures; balance and convergence research; neural community versions; supervised studying and unsupervised studying; kernel equipment and aid vector machines; combination types and clustering; visible belief and trend attractiveness; movement, monitoring and item attractiveness; traditional scene research and speech attractiveness; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent platforms and adaptive dynamic programming; reinforcement studying and determination making; motion and motor regulate; adaptive and hybrid clever structures; neuroinformatics and bioinformatics; details retrieval; info mining and information discovery; and average language processing.

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Extra info for Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II

Example text

We first generate a count x and then use the 40 P. Tiˇ no A-C statistic PAC (y|x) (2) to define a distribution over y, given the already observed count x.

For a transcript representing a small fraction of the library and a large number N of clones, the probability of observing x tags of the same gene will be well-approximated by the Poisson distribution parametrized by λ ≥ 0: λx . (1) x! The unknown parameter λ signifies the number of transcripts of the given type (tag) per N clones in the cDNA library. The probability of count y, given the observed count x from the same (unknown) Poisson distribution is: P (X = x|λ) = e−λ ∞ PAC (y|x) = 0 = ∞ P (y|λ) p(λ|x) dλ P (y|λ) 0 ∞ 0 P (x|λ) p(λ) dλ.

Under the null hypothesis, the quantity of interest is the probability of observing y occurrences of a clone already observed x times. For a transcript representing a small fraction of the library and a large number N of clones, the probability of observing x tags of the same gene will be well-approximated by the Poisson distribution parametrized by λ ≥ 0: λx . (1) x! The unknown parameter λ signifies the number of transcripts of the given type (tag) per N clones in the cDNA library. The probability of count y, given the observed count x from the same (unknown) Poisson distribution is: P (X = x|λ) = e−λ ∞ PAC (y|x) = 0 = ∞ P (y|λ) p(λ|x) dλ P (y|λ) 0 ∞ 0 P (x|λ) p(λ) dλ.

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