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Titlebook: Case-Based Reasoning on Images and Signals; Petra Perner Book 2008 Springer-Verlag Berlin Heidelberg 2008 Case-Based Reasoning.Signal.Stat

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书目名称Case-Based Reasoning on Images and Signals
编辑Petra Perner
视频video
概述First edited book that deals with the special topic of signals and images within Case-Based Reasoning (CBR).Includes supplementary material:
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Case-Based Reasoning on Images and Signals;  Petra Perner Book 2008 Springer-Verlag Berlin Heidelberg 2008 Case-Based Reasoning.Signal.Stat
描述This book is the ?rst edited book that deals with the special topic of signals and images within case-based reasoning (CBR). Signal-interpreting systems are becoming increasingly popular in medical, industrial, ecological, biotechnological and many other applications. Existing statisticalandknowledge-basedtechniqueslackrobustness,accuracy,and?- ibility. New strategies are needed that can adapt to changing environmental conditions, signal variation, user needs and process requirements. Introducing CBRstrategiesintosignal-interpretingsystemscansatisfytheserequirements. CBR can be used to control the signal-processing process in all phases of a signal-interpreting system to derive information of the highest possible qu- ity. Beyond this CBR o?ers di?erent learning capabilities, for all phases of a signal-interpretingsystem,thatsatisfydi?erentneedsduringthedevelopment process of a signal-interpreting system. In the outline of this book we summarize under the term “signal” signals of 1-dimensional, 2-dimensional or 3-dimensional nature. The unique data and the necessary computation techniques require ext- ordinary case representations, similarity measures and CBR strategies to be utilis
出版日期Book 2008
关键词Case-Based Reasoning; Signal; Statistica; classification; computational intelligence; image processing; im
版次1
doihttps://doi.org/10.1007/978-3-540-73180-1
isbn_softcover978-3-642-09221-3
isbn_ebook978-3-540-73180-1Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2008
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Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Datginal, and usually real-valued time series data. These features should on one hand convey significant information to human experts enabling potential discoveries/findings and on the other hand facilitate much simplified case indexing and similarity matching in case-based reasoning. The road map to a
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Case-Based Reasoning for Image Segmentation by Watershed Transformation,ed to improve the performance of the adopted segmentation method and to ensure that good segmentation results are obtained even if the segmentation method is applied to images with different characteristics. In practice, CBR will select from a case-base the cases having image characteristics similar
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https://doi.org/10.1007/978-1-4842-5422-6duced to utility..In order to solve problems some knowledge is necessary. This knowledge has different sources and can be used in different ways. Therefore, we put some emphasis on the question what kind of knowledge is needed, what is contained in a measure and how is it entered into a system. This
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Algorithms for Numerical Analysis,ough an historical perspective referring to the theory of the dynamic memory, and finally develops the two main types of learning related to CBR memories, namely mining for memory structures and mining for memory organization.
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