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Journal ArticleDOI

Pattern Recognition and Machine Learning

Radford M. Neal
- 01 Aug 2007 - 
- Vol. 49, Iss: 3, pp 366-366
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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.

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Citations
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Image Quality Assessment Using Multi-Method Fusion

TL;DR: The proposed MMF method using support vector regression is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases.
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Deep Convolutional Highway Unit Network for SAR Target Classification With Limited Labeled Training Data

TL;DR: Experimental results on the moving and stationary target acquisition and recognition data set indicate that the branched ensemble model based on the unit architecture can achieve 99% classification accuracy with all training data.
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Reconfigurable models for scene recognition

TL;DR: A new latent variable model for scene recognition that represents a scene as a collection of region models arranged in a reconfigurable pattern and uses a latent variable to specify which region model is assigned to each image region.
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Deep Multimodal Clustering for Unsupervised Audiovisual Learning

TL;DR: Deep Multimodal Clustering (DMC) as discussed by the authors performs sets of clustering with multimodal vectors of convolutional maps in different shared spaces for capturing multiple audiovisual correspondences.
Proceedings ArticleDOI

3D Pictorial Structures for Multiple View Articulated Pose Estimation

TL;DR: It is shown that it is possible and tractable to extend the pictorial structures framework, popular for 2D pose estimation, to 3D, and how to use this framework to impose view, skeleton, joint angle and intersection constraints in 3D is discussed.