Journal ArticleDOI
Pattern Recognition and Machine Learning
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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.read more
Citations
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Proceedings ArticleDOI
Fast Keypoint Recognition in Ten Lines of Code
TL;DR: This paper shows that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust, and it scales well to handle large number of classes.
Book
State Estimation for Robotics
TL;DR: In this paper, the authors present common sensor models and practical advice on how to carry out state estimation for rotations and other state variables, including batch estimation, the Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection and continuous-time trajectory estimation.
Proceedings ArticleDOI
Adversarially Learned One-Class Classifier for Novelty Detection
TL;DR: In this paper, the authors proposed an end-to-end architecture for one-class classification, which consists of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
Journal ArticleDOI
Disambiguation and co-authorship networks of the U.S. patent inventor database (1975-2010)
Guan-Cheng Li,Ronald K. Lai,Alexander D’Amour,David M. Doolin,Ye Sun,Vetle I. Torvik,Amy Z. Yu,Lee Fleming +7 more
TL;DR: The paper provides an overview of the disambiguation method, assesses its accuracy, and calculates network measures based on co-authorship and collaboration variables, and illustrates the potential for large-scale innovation studies across time and space with visualizations of inventor mobility across the United States.
Journal ArticleDOI
Non-linear regression models for Approximate Bayesian Computation
TL;DR: A machine-learning approach to the estimation of the posterior density by introducing two innovations that fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling.