Titlebook: Mastering the Discrete Fourier Transform in One, Two or Several Dimensions; Pitfalls and Artifac Isaac Amidror Book 2013 Springer-Verlag Lo

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書目名稱Mastering the Discrete Fourier Transform in One, Two or Several Dimensions影響因子(影響力)學(xué)科排名




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書目名稱Mastering the Discrete Fourier Transform in One, Two or Several Dimensions被引頻次




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1381-6446 r Transform in One, Two or Several Dimensions. is intended for scientists, engineers, students and any readers who wish to widen their knowledge of the DFT and its practical use. This book will also be very use978-1-4471-6131-8978-1-4471-5167-8Series ISSN 1381-6446

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end the topological model to analyze it. Through experiments we show that our algorithm performs well in indexing an image database for relevance feedback. With our indexing procedure, the average response time to access image results from a storage device is lower when compared to vector quantizati

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Isaac Amidrorn. The descriptions of the methods are followed by an extensive empirical study over 20 publicly available datasets. Finally a comparison has been done with genetic algorithm, which shows the effectiveness of hill climbing methods in the context of feature selection.

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Isaac Amidrornance & economics at the IEEE Congress on Evolutionary Computation, the IEEE symposium on Computational Intelligence for Financial Engineering (CIFEr), the annual international conference on Computational Intelligence in Economics & Finance (CIEF), and the European workshop on Evolutionary Computati

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Isaac Amidrorrom the rest. The phenotype however, is, in principle, observable. A microscopic description at the level of the set of individual trades, however, is not sufficient to understand or characterize the strategies at a more macroscopic and intuitive one. By introducing a set of coarse grained variables

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Isaac Amidrorrom the rest. The phenotype however, is, in principle, observable. A microscopic description at the level of the set of individual trades, however, is not sufficient to understand or characterize the strategies at a more macroscopic and intuitive one. By introducing a set of coarse grained variables

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