Journal ArticleDOI
Pattern Recognition and Machine Learning
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
Target Classification Using the Deep Convolutional Networks for SAR Images
TL;DR: A new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used, which can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.
Journal ArticleDOI
POMDP-Based Statistical Spoken Dialog Systems: A Review
TL;DR: This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems.
Journal ArticleDOI
SATzilla: portfolio-based algorithm selection for SAT
TL;DR: SATzilla is described, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers and is improved by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances.
Proceedings ArticleDOI
Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation
Sam Johnson,Mark Everingham +1 more
TL;DR: A new annotated database of challenging consumer images is introduced, an order of magnitude larger than currently available datasets, and over 50% relative improvement in pose estimation accuracy over a state-of-the-art method is demonstrated.
Book
A Survey on Policy Search for Robotics
TL;DR: This work classifies model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and presents a unified view on existing algorithms.