Partially Supervised Learning Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected

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Free Download Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers By Gabriel B. P. Costa, Moacir Ponti, Alejandro C. Frery (auth.), Zhi-Hua Zhou, Friedhelm Schwenker (eds.)
2013 | 117 Pages | ISBN: 3642407048 | PDF | 3 MB
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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