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008 131021s2013 gw | s |||| 0|eng d
020 _a9783642407055
_9978-3-642-40705-5
024 7 _a10.1007/978-3-642-40705-5
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
245 1 0 _aPartially Supervised Learning
_h[electronic resource] :
_bSecond IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers /
_cedited by Zhi-Hua Zhou, Friedhelm Schwenker.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aIX, 117 p. 34 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v8183
505 0 _aPartially 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.
520 _aThis 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.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aPattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aPattern Recognition.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aZhou, Zhi-Hua.
_eeditor.
700 1 _aSchwenker, Friedhelm.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642407048
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v8183
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-40705-5
912 _aZDB-2-SCS
912 _aZDB-2-LNC
942 _cEBK
999 _c53566
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