Results 1 - 10 of 10,241

General methods for monitoring convergence of iterative simulations

by Stephen P. Brooksand, Andrew Gelman - J. Comput. Graph. Statist , 1998
"... We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order to develo ..."
Abstract - Cited by 551 (8 self) - Add to MetaCart
We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order

Graph Cuts and Efficient N-D Image Segmentation

by Yuri Boykov, Gareth Funka-Lea , 2006
"... Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graph-cuts: segmentation of objects in image data. Despite its simplicity, this application epitomizes the best features ..."
Abstract - Cited by 307 (7 self) - Add to MetaCart
was widely studied in computer vision and graphics communities. We provide links to a large number of known extensions based on iterative parameter re-estimation and learning, multi-scale or hierarchical approaches, narrow bands, and other techniques for demanding photo, video, and medical applications.

Active Appearance Models.

by Timothy F Cootes , Gareth J Edwards , Christopher J Taylor - IEEE Transactions on Pattern Analysis and Machine Intelligence, , 2001
"... AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations ..."
Abstract - Cited by 2154 (59 self) - Add to MetaCart
AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations

Pegasos: Primal Estimated sub-gradient solver for SVM

by Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter
"... We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a singl ..."
Abstract - Cited by 542 (20 self) - Add to MetaCart
single training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require Ω(1/ɛ2) iterations. As in previously devised SVM solvers, the number of iterations also scales linearly with 1/λ, where λ is the regularization parameter of SVM. For a linear kernel, the total

Probabilistic Principal Component Analysis

by Michael E. Tipping, Chris M. Bishop - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of paramet ..."
Abstract - Cited by 709 (5 self) - Add to MetaCart
of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach

Route Packets, Not Wires: On-Chip Interconnection Networks

by William J. Dally, Brian Towles , 2001
"... Using on-chip interconnection networks in place of ad-hoc global wiring structures the top level wires on a chip and facilitates modular design. With this approach, system modules (processors, memories, peripherals, etc...) communicate by sending packets to one another over the network. The structur ..."
Abstract - Cited by 885 (10 self) - Add to MetaCart
. The structured network wiring gives well-controlled electrical parameters that eliminate timing iterations and enable the use of high-performance circuits to reduce latency and increase bandwidth. The area overhead required to implement an on-chip network is modest, we estimate 6.6%. This paper introduces

Inducing Features of Random Fields

by Stephen Della Pietra, Vincent Della Pietra, John Lafferty - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract - Cited by 670 (10 self) - Add to MetaCart
the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques

Greedy Randomized Adaptive Search Procedures

by Mauricio G. C. Resende , Celso C. Ribeiro , 2002
"... GRASP is a multi-start metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
Abstract - Cited by 647 (82 self) - Add to MetaCart
GRASP is a multi-start metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search

Conditional random fields: Probabilistic models for segmenting and labeling sequence data

by John Lafferty , 2001
"... We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions ..."
Abstract - Cited by 3485 (85 self) - Add to MetaCart
made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. We present iterative parameter estimation

HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks

by Ossama Younis, Sonia Fahmy - IEEE TRANS. MOBILE COMPUTING , 2004
"... Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed ..."
Abstract - Cited by 590 (1 self) - Add to MetaCart
according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in Oð1Þ iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate
Results 1 - 10 of 10,241