Journal ArticleDOI
Pattern Recognition and Machine Learning
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TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
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Journal ArticleDOI
Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine
TL;DR: It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images.
Proceedings ArticleDOI
Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns
Ye Xu,Mu Lin,Hong Lu,Giuseppe Cardone,Nicholas D. Lane,Zhenyu Chen,Andrew T. Campbell,Tanzeem Choudhury +7 more
TL;DR: An app usage prediction model that leverages three key everyday factors that affect app usage decisions, including intrinsic user app preferences and user historical patterns, and user activities and the environment as observed through sensor-based contextual signals is developed.
Journal Article
Cluster-based reduced-order modelling of a mixing layer
Eurika Kaiser,Bernd R. Noack,Laurent Cordier,Andreas Spohn,Marc Segond,Markus Abel,Guillaume Daviller,Robert K. Niven +7 more
Journal ArticleDOI
Local Evidence Aggregation for Regression-Based Facial Point Detection
TL;DR: A new algorithm to detect facial points in frontal and near-frontal face images is proposed that combines a regression-based approach with a probabilistic graphical model-based face shape model that restricts the search to anthropomorphically consistent regions.
Journal ArticleDOI
Predicting Adoption Probabilities in Social Networks
TL;DR: In this paper, the authors identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors, and develop the locally weighted expectation-maximization method for Naive Bayesian learning to predict adoption probabilities on the basis of these factors.