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
A Family of Nonparametric Density Estimation Algorithms
Esteban G. Tabak,Cristina Turner +1 more
TL;DR: A new methodology for density estimation that builds on the one developed by Tabak and Vanden‐Eijnden, normalizes the data points through the composition of simple maps and determines the parameters of each map through the maximization of a local quadratic approximation to the log‐likelihood.
Journal ArticleDOI
Robust network traffic classification
TL;DR: The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes and is significantly better than four state-of-the-art methods.
Journal ArticleDOI
Automatic Video Classification: A Survey of the Literature
Darin Brezeale,Diane J. Cook +1 more
TL;DR: This paper surveys the video classification literature and finds that features are drawn from three modalities - text, audio, and visual - and that a large variety of combinations of features and classification have been explored.
Proceedings ArticleDOI
Modeling User Exposure in Recommendation
TL;DR: This paper proposes a new probabilistic approach that directly incorporates user exposure to items into collaborative filtering, and recovers one of the most successful state-of-the-art approaches as a special case of the model.
Journal ArticleDOI
A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy
Neale P. Gibson,Suzanne Aigrain,Steve G. Roberts,Thomas M. Evans,Michael A. Osborne,Frederic Pont +5 more
TL;DR: In this paper, the authors proposed a nonparametric Gaussian Process (GP) method to infer transit parameters in the presence of systematic noise using Gaussian processes, a technique widely used in the machine learning community for Bayesian regression and classification problems.