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Titlebook: Similarity-Based Pattern Recognition; Second International Edwin Hancock,Marcello Pelillo Conference proceedings 2013 Springer-Verlag Berli

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書目名稱Similarity-Based Pattern Recognition
副標題Second International
編輯Edwin Hancock,Marcello Pelillo
視頻videohttp://file.papertrans.cn/868/867414/867414.mp4
概述Conference proceedings of the International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2013
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Similarity-Based Pattern Recognition; Second International Edwin Hancock,Marcello Pelillo Conference proceedings 2013 Springer-Verlag Berli
描述This book constitutes the proceedings of the Second International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2013, which was held in York, UK, in July 2013. The 18 papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, from theoretical issues to real-world practical applications, and offer a timely picture of the state of the art in the field.
出版日期Conference proceedings 2013
關(guān)鍵詞bio-cryptography; generative models; graph theory; quantum walks; structural complexity; algorithm analys
版次1
doihttps://doi.org/10.1007/978-3-642-39140-8
isbn_softcover978-3-642-39139-2
isbn_ebook978-3-642-39140-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2013
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Break and Conquer: Efficient Correlation Clustering for Image Segmentation,ing the characteristics of the image segmentation problem that make it possible to break the problem into subproblems. Each sub-problem corresponds to an automatically detected image part. We demonstrate a reduced computational complexity with comparable results to state-of-the-art on the BSDS-500 and the Weizmann Two-Objects datasets.
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Fast Learning of Gamma Mixture Models with ,-MLE, step to choose this parameter is added in the algorithm. Since it converges locally and is computationally faster than an Expectation-Maximization method for Gamma mixture models, our method can be used beneficially as a drop-in replacement in any application using this kind of statistical models.
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Conference proceedings 2013s held in York, UK, in July 2013. The 18 papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, from theoretical issues to real-world practica
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