New PDF release: 7th Int'l Conference on Numerical Methods in Fluid Dynamics

By W. C. Reynolds, R. W. MacCormack

ISBN-10: 3540106944

ISBN-13: 9783540106944

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3 HCS: Fundamentals HCS (Hypothesis testing with Classifier Systems) is the learning algorithm we developed and employed to analyze the oral cancer dataset. The underlying idea driving HCS design was to construct a system which could find interesting information in a dataset. Formally defining interestingness immediately appeared very difficult, if not impossible at all: it is too much a subjective and domain-dependent quantity, to be practically defined in all situations. We then turned our research for the interesting into a research for the unexpected.

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In blocks of equal size). Each block is processed individually. The input of PDA is a set of (blocks of) runs described by a list of triplets ( m/z, RT, run id) consisting of ’m/z’ value, ’RT’ value and run identifier. The output of PDA is a set of clusters of (m/z, RT) pairs representing peaks, together with information about their signal intensity. Novel features of PDA include: 1. a similarity measure for comparing features, where a feature is a triplet (m/z, RT, id). Weights are associated to each of the three attributes to specify their relevance; 2.

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7th Int'l Conference on Numerical Methods in Fluid Dynamics by W. C. Reynolds, R. W. MacCormack


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