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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Zhou, Zhi-Hua (Επιμελητής έκδοσης), Schwenker, Friedhelm (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Σειρά: Lecture Notes in Computer Science, 8183
• Θέματα: Computer science.; Data mining.; Artificial intelligence.; Pattern recognition.; Computer Science.; Data Mining and Knowledge Discovery.; Pattern Recognition.; Artificial Intelligence (incl. Robotics).

 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. 

 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.