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Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Antonio Robles-Kelly,Marco Loog,Richard Wilson Conference

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書目名稱Structural, Syntactic, and Statistical Pattern Recognition
副標題Joint IAPR Internati
編輯Antonio Robles-Kelly,Marco Loog,Richard Wilson
視頻videohttp://file.papertrans.cn/881/880090/880090.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Antonio Robles-Kelly,Marco Loog,Richard Wilson Conference
描述.This book constitutes the proceedings of the Joint IAPR International Workshop on Structural?Syntactic, and Statistical Pattern Recognition, S+SSPR 2016, consisting of the International Workshop on Structural and Syntactic Pattern Recognition SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The 51 full papers presented were carefully reviewed and selected from 68 submissions. They are organized in the following topical sections: dimensionality reduction, manifold learning and embedding methods; dissimilarity representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis; spatio-temporal pattern recognition; structural matching; text and document analysis.?.
出版日期Conference proceedings 2016
關(guān)鍵詞complex networks; machine learning; optimization; semantic segmentation; visualization; artificial intell
版次1
doihttps://doi.org/10.1007/978-3-319-49055-7
isbn_softcover978-3-319-49054-0
isbn_ebook978-3-319-49055-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2016
The information of publication is updating

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GriMa: A Grid Mining Algorithm for Bag-of-Grid-Based Classificationuld be beneficial to obtain more discriminative features. Experiments on different datasets show that our algorithm is efficient and that adding the structure may greatly help the image classification process.
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Correlation Network Evolution Using Mean Reversion Autoregressionobtain a more meaningful mean reversion term. We show experimentally that the dynamic network model can be used to recover detailed statistical properties of the original network data. More importantly, it also suggests that the model is effective in analyzing the predictability of stock correlation networks.
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