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131021s2013 gw | s |||| 0|eng d |
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|a 9783642407055
|9 978-3-642-40705-5
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|a 10.1007/978-3-642-40705-5
|2 doi
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|a QA76.9.D343
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|a 006.312
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|a Zhou, Zhi-Hua.
|e editor.
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|a Partially Supervised Learning
|b Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers /
|c edited by Zhi-Hua Zhou, Friedhelm Schwenker.
|h [electronic resource] :
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2013.
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|a IX, 117 p. 34 illus.
|b online resource.
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|a text
|b txt
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|a computer
|b c
|2 rdamedia
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|a online resource
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|a text file
|b PDF
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|a Lecture Notes in Computer Science,
|v 8183
|x 0302-9743 ;
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| 505 |
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|a Partially Supervised Anomaly Detection using Convex Hulls on a 2D Parameter Space -- Self-Practice Imitation Learning from Weak Policy -- Semi-Supervised Dictionary Learning of Sparse Representations for Emotion Recognition -- Adaptive Graph Constrained NMF for Semi-Supervised Learning -- Kernel Parameter Optimization in Stretched Kernel-based Fuzzy Clustering -- Conscientiousness Measurement from Weibo s Public Information -- Meta-Learning of Exploration and Exploitation Parameters with Replacing Eligibility Traces -- Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data -- A Robust Image Watermarking Scheme Based on BWT and ICA -- A New Weighted Sparse Representation Based on MSLBP and Its Application to Face Recognition.
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|a This book constitutes the thoroughly refereed revised selected papers from the Second IAPR International Workshop, PSL 2013, held in Nanjing, China, in May 2013. The 10 papers included in this volume were carefully reviewed and selected from 26 submissions. Partially supervised learning is a rapidly evolving area of machine learning. It generalizes many kinds of learning paradigms including supervised and unsupervised learning, semi-supervised learning for classification and regression, transductive learning, semi-supervised clustering, multi-instance learning, weak label learning, policy learning in partially observable environments, etc.
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| 650 |
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|a Computer science.
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| 650 |
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|a Data mining.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Optical pattern recognition.
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| 650 |
1 |
4 |
|a Computer Science.
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| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
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| 650 |
2 |
4 |
|a Pattern Recognition.
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| 650 |
2 |
4 |
|a Artificial Intelligence (incl. Robotics).
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| 700 |
1 |
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|a Schwenker, Friedhelm.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9783642407048
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| 830 |
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|a Lecture Notes in Computer Science,
|v 8183
|x 0302-9743 ;
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| 856 |
4 |
0 |
|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-40705-5
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-LNC
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| 950 |
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|a Computer Science (Springer-11645)
|