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Journal ArticleDOI

Pattern Recognition and Machine Learning

01 Aug 2007-Technometrics (Taylor & Francis)-Vol. 49, Iss: 3, pp 366-366
TL;DR: 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.
Citations
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Proceedings ArticleDOI
10 Apr 2011
TL;DR: Time-series analysis techniques are used to automatically predict the online population, the peer upload and the server bandwidth demand in each video channel, based on the learning of both human factors and system dynamics from online measurements.
Abstract: Peer-assisted on-demand video streaming services are extremely large-scale distributed systems on the Internet. Automated demand forecast and performance prediction, if implemented, can help with capacity planning and quality control so that sufficient server bandwidth can always be supplied to each video channel without incurring wastage. In this paper, we use time-series analysis techniques to automatically predict the online population, the peer upload and the server bandwidth demand in each video channel, based on the learning of both human factors and system dynamics from online measurements. The proposed mechanisms are evaluated on a large dataset collected from a commercial Internet video-on-demand system.

126 citations


Cites methods from "Pattern Recognition and Machine Lea..."

  • ...For each class, we train the mixture of Gaussians (5) with k = 5 using the EM algorithm [7] based on data {Nt; 0 < t ≤ 144n} from the training channels....

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Journal ArticleDOI
TL;DR: IRootLab is a free and open-source MATLAB toolbox for vibrational biospectroscopy (VBS) data analysis that offers an object-oriented programming class library, graphical user interfaces (GUIs) and automatic MATLAB code generation.
Abstract: Summary: IRootLab is a free and open-source MATLAB toolbox for vibrational biospectroscopy (VBS) data analysis. It offers an object-oriented programming class library, graphical user interfaces (GUIs) and automatic MATLAB code generation. The class library contains a large number of methods, concepts and visualizations for VBS data analysis, some of which are introduced in the toolbox. The GUIs provide an interface to the class library, including a module to merge several spectral files into a dataset. Automatic code allows developers to quickly write VBS data analysis scripts and is a unique resource among tools for VBS. Documentation includes a manual, tutorials, Doxygen-generated reference and a demonstration showcase. IRootLab can handle some of the most popular file formats used in VBS. License: GNU-LGPL. Availability: Official website: http://irootlab.googlecode.com/. Contact: juliotrevisan@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

126 citations


Cites background or methods from "Pattern Recognition and Machine Lea..."

  • ...It is a highly modular software that was created based on existing theory of data analysis (Alpaydin, 2004; Bishop, 2006; Duda et al., 2001; Guyon et al., 2006; Hastie et al., 2007; Kuncheva, 2004), and as such, applied to VBS (Griffiths and Haseth, 2007; Somorjai, 2009), bringing together families…...

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  • ...Received on December 3, 2012; revised on February 8, 2013; accepted on February 13, 2013...

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Journal ArticleDOI
TL;DR: This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches.
Abstract: Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches.

126 citations


Cites background from "Pattern Recognition and Machine Lea..."

  • ...Generative models are very popular in the machine learning community, with many variations in existence [e.g. Roweis and Ghahramani 1999, Bishop 2006, Buxton 2003]....

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  • ...Generative models are very popular in the machine learning community, with many variations in existence (e.g. Roweis and Ghahramani 1999; Bishop 2006; Buxton 2003)....

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Journal ArticleDOI
TL;DR: A super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.
Abstract: Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.

126 citations

Journal ArticleDOI
TL;DR: It is proposed that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations.
Abstract: General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling.

126 citations


Cites background or methods from "Pattern Recognition and Machine Lea..."

  • ...This effect is known as overfitting [6, 7]....

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  • ...We demonstrate in Fig 2D and 2F the improved generalization capability of this model for the learning approach Eq (2) (learning of the posterior), compared with maximum likelihood learning (approach Eq (1)), which had been theoretically predicted by [6] and [7]....

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  • ...This new model satisfies theoretical requirements for handling priors such as structural constraints and rules in a principled manner, that have previously already been formulated and explored in the context of artificial neural networks [6, 7], as well as more recent challenges that arise from probabilistic brain models [8]....

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  • ...Thus the learning process approximates for low values of Tmaximum a posteriori (MAP) inference [7]....

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  • ...A thorough discussion on this topic which is known as Bayesian regularization can be found in [6, 7]....

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