Titlebook: Computer Recognition Systems 3; Marek Kurzynski,Michal Wozniak Book 2009 Springer-Verlag Berlin Heidelberg 2009 artificial intelligence.co

CBC471

P. Øyvind Nordli,Arne A. Grimenesnvestigated. Then, its empirical version is discussed and a learning algorithm for an orthogonal neural net is proposed, which takes context variables into account. The proposed algorithm has a recursive form, which is well suited for learning from a stream of patterns, which arise when features are

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AGONY

The Audit Committee in Context,cations like for example digital zoom in camcorders or conversion between conventional TV content and high resolution HDTV format. In general the main problem when converting an image to higher resolution is the lack of high frequency components in the resulting image. The result is the blurry aspec

SLING

anticipate

Stricture

A Story about Collecting Stories,sulting from representation of a mesh fragment with a set of symbols. We view this set of symbols as helpful for 3D object retrieval and comparision purposes, however here we focus solely on representation errors. We propose a mesh quantization scheme using Linde-Buzo-Gray (LBG) algorithm with appro

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Notes on the Theory of Actor? Networks,ages. Moreover, it is shown that unsupervised selection of significant texture parameters in the datasets corresponding to noisy images becomes feasible if the search for relevant attributes is guided by the clustering stability–based optimization criterion.

滔滔不绝的人

The Audit Committee in Context,ation approaches have been used: the Normal Bayes Classifier, the Type 1 Linear Support Vector Machine (LSVM) and the type 2 LSVM with a soft margin. The influence of the shape extraction algorithm parameters on the classification efficiency has been investigated. The experiments were conducted on a

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