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
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Factor Graphs for Robot Perception
Frank Dellaert,Michael Kaess +1 more
TL;DR: The use of factor graphs for the modeling and solving of large-scale inference problems in robotics is reviewed, and the iSAM class of algorithms that can reuse previous computations are discussed, re-interpreting incremental matrix factorization methods as operations on graphical models, introducing the Bayes tree in the process.
Journal ArticleDOI
A review of algorithms for medical image segmentation and their applications to the female pelvic cavity
TL;DR: The main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed.
Posted Content
PUF Modeling Attacks on Simulated and Silicon Data.
Ulrich Rührmair,Jan Sölter,Frank Sehnke,Xiaolin Xu,Ahmed Mahmoud,Vera Stoyanova,Gideon Dror,Jürgen Schmidhuber,Wayne Burleson,Srinivas Devadas +9 more
TL;DR: In this article, numerical modeling attacks on several PUFs are discussed. But the authors focus on strong PUFs, and do not consider weak PUFs such as XOR Arbiter PUFs and Lightweight Secure PUFs.
Journal ArticleDOI
Applications of machine learning in animal behaviour studies
TL;DR: This review aims to introduce animal behaviourists unfamiliar with machine learning (ML) to the promise of these techniques for the analysis of complex behavioural data and illustrate key ML approaches by developing data analytical pipelines for three different case studies that exemplify the types of behavioural and ecological questions ML can address.
Journal ArticleDOI
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
TL;DR: This paper considers feature selection for data classification in the presence of a huge number of irrelevant features, and proposes a new feature-selection algorithm that addresses several major issues with prior work, including problems with algorithm implementation, computational complexity, and solution accuracy.