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
Citations
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Journal ArticleDOI
Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts
TL;DR: A state-of-the-art ensemble learner Stacked Generalization that combines several classifiers that scales to large scale industrial application and that it outperforms the use of individual classifiers for bug assignment, reaching prediction accuracies from 50 % to 89 % when large training sets are used.
Book
Learning Motor Skills: From Algorithms to Robot Experiments
Jens Kober,Jan Peters +1 more
TL;DR: This book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms.
Proceedings ArticleDOI
CPA-SLAM: Consistent plane-model alignment for direct RGB-D SLAM
TL;DR: A real-time capable RGB-D SLAM system that consistently integrates frame-to-keyframe and frame- to-plane alignment and uses the planes for tracking and global graph optimization in an expectation-maximization framework.
Journal ArticleDOI
Scan statistics for the online detection of locally anomalous subgraphs
TL;DR: This work introduces a computationally scalable method for detecting small anomalous areas in a large, time-dependent computer network, motivated by the challenge of identifying intruders operating inside enterprise-sized computer networks.
Journal ArticleDOI
Unsupervised Classification of High-Frequency Oscillations in Human Neocortical Epilepsy and Control Patients
Justin A. Blanco,Matt Stead,Abba M. Krieger,Jonathan Viventi,W. Richard Marsh,Kendall H. Lee,Gregory A. Worrell,Brian Litt +7 more
TL;DR: An algorithm for detecting and classifying these signals automatically is introduced and the tractability of analyzing a data set of unprecedented size, over 31,000 channel-hours of intracranial electroencephalographic recordings from micro- and macroelectrodes in humans is demonstrated.