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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Data-driven computational mechanics
TL;DR: A new computing paradigm is developed, which is referred to as data-driven computing, according to which calculations are carried out directly from experimental material data and pertinent constraints and conservation laws, thus bypassing the empirical material modeling step of conventional computing altogether.
Journal ArticleDOI
Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior
TL;DR: It is shown that it is an extraordinary property of biological networks that sophisticated behavior is able to emerge from simple interactions among lower-level agents.
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A survey on nature inspired metaheuristic algorithms for partitional clustering
TL;DR: An up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering and key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
Proceedings ArticleDOI
Joint latent topic models for text and citations
TL;DR: This work addresses the problem of joint modeling of text and citations in the topic modeling framework with two different models called the Pairwise-Link-LDA and the Link-PLSA-Lda models, which combine the LDA and PLSA models into a single graphical model.
Journal ArticleDOI
3D Traffic Scene Understanding From Movable Platforms
TL;DR: A novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene is presented.