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Titlebook: Data Complexity in Pattern Recognition; Mitra Basu,Tin Kam Ho Book 2006 Springer-Verlag London 2006 algorithm.algorithms.classification.cl

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发表于 2025-3-21 17:29:47 | 显示全部楼层 |阅读模式
书目名称Data Complexity in Pattern Recognition
编辑Mitra Basu,Tin Kam Ho
视频video
概述Shows how to appreciate the presence and nature of patterns in specific problems.Helps the reader set proper expectations for classification performance.Offers guidance on choosing the best pattern re
丛书名称Advanced Information and Knowledge Processing
图书封面Titlebook: Data Complexity in Pattern Recognition;  Mitra Basu,Tin Kam Ho Book 2006 Springer-Verlag London 2006 algorithm.algorithms.classification.cl
描述.Machines capable of automatic pattern recognition have many fascinating uses in science & engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability...This book takes a close view of data complexity & its role in shaping the theories & techniques in different disciplines & asks:...What is missing from current classification techniques?..When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task?..How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data?..Uunique in its comprehensive coverage & multidisciplinary approach from various methodological & practical perspectives, researchers & practitioners will find this book an insightful reference to learn about current available techniques as well as application areas..
出版日期Book 2006
关键词algorithm; algorithms; classification; clustering; cognition; complexity; evolution; graph; human-computer i
版次1
doihttps://doi.org/10.1007/978-1-84628-172-3
isbn_softcover978-1-84996-557-6
isbn_ebook978-1-84628-172-3Series ISSN 1610-3947 Series E-ISSN 2197-8441
issn_series 1610-3947
copyrightSpringer-Verlag London 2006
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Samiha Ouda,Abd El-Hafeez Zohryning tasks. The combinatoric problems usually attached to these tasks prove to be indeed difficult. The third level relates the objects to the classes. Membership may be problematic, and this is even more the case when approximations (of the strings or the languages) are used, for instance in a nois
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https://doi.org/10.1007/978-3-030-18206-9 problems are shown to be almost equal to the value predicted from the average radius of the class centroids. The class-conditional distributions of the patterns are compared using two measures of divergence. The difference between the distributions of the same class with different feature sets is f
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Climate and Life in the Caribbean Basining ability between humans and machines. Thus, many technical issues studied by the image recognition research community are relevant to HIPs. This chapter describes the evolution of HIP R&D, applications of HIPs now and on the horizon, relevant legal issues, highlights of the first two HIP workshop
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Simple Statistics for Complex Feature Spaces problems are shown to be almost equal to the value predicted from the average radius of the class centroids. The class-conditional distributions of the patterns are compared using two measures of divergence. The difference between the distributions of the same class with different feature sets is f
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