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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Book ChapterDOI
Two problems with variational expectation maximisation for time-series models
Richard E. Turner,Maneesh Sahani +1 more
TL;DR: In this paper, the success of variational expectation maximization (vEM) in simple probabilistic time series models is investigated, and it is shown that simpler variational approximations (such as mean-field) can lead to less bias than more complicated structured approximate.
Journal ArticleDOI
Improving the driver-automation interaction: an approach using automation uncertainty.
TL;DR: The presentation of automation uncertainty through a symbol improves overall driver–automation cooperation and might improve the acceptance of fallible systems and further enhances driver–AUTOMation cooperation.
Journal ArticleDOI
Rigid and Articulated Point Registration with Expectation Conditional Maximization
TL;DR: An innovative EM-like algorithm, namely, the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm, is introduced, which allows the use of general covariance matrices for the mixture model components and improves over the isotropic covariance case.
Generative or Discriminative? Getting the Best of Both Worlds
Julia Lasserre,Christopher M. Bishop,José M. Bernardo,Maria J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman,Adrian F. M. Smith,Mike West +8 more
TL;DR: This paper presents an approach to finding the conditional distribution p(c|x) using a parametric model, and then to determine the parameters using a training set consisting of pairs of input vectors along with their corresponding target output vectors.
Journal ArticleDOI
Learning to detect malicious URLs
TL;DR: This article develops a real-time system for gathering URL features and is able to train an online classifier that detects malicious Web sites with 99% accuracy over a balanced dataset.