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Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

by Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein , 2010
"... ..."
Abstract - Cited by 1001 (20 self) - Add to MetaCart
Abstract not found

Probabilistic Inference Using Markov Chain Monte Carlo Methods

by Radford M. Neal , 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. R ..."
Abstract - Cited by 736 (24 self) - Add to MetaCart
physics for over forty years, and, in the last few years, the related method of "Gibbs sampling" has been applied to problems of statistical inference. Concurrently, an alternative method for solving problems in statistical physics by means of dynamical simulation has been developed as well

Stochastic volatility: likelihood inference and comparison with ARCH models

by Sangjoon Kim, Salomon Brothers, Asia Limited, Neil Shephard - Review of Economic Studies , 1998
"... In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offse ..."
Abstract - Cited by 592 (40 self) - Add to MetaCart
offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non

Alternative Methods

by Chengkai Li, Christoph Csallner, Future Work , 2010
"... Implementation ..."
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Implementation

Alternative Method for

by John Shainoff, Patricia Dibello, Ruth Earley, Gary B. Smejkal, Rudolf M, Henry F. Hoff
"... alized on polyvinylidene difluoride membranes ..."
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alized on polyvinylidene difluoride membranes

Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms

by Michael Collins , 2002
"... We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modific ..."
Abstract - Cited by 660 (13 self) - Add to MetaCart
We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a

An experimental comparison of three methods for constructing ensembles of decision trees

by Thomas G. Dietterich, Doug Fisher - Bagging, boosting, and randomization. Machine Learning , 2000
"... Abstract. Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base ” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approac ..."
Abstract - Cited by 610 (6 self) - Add to MetaCart
Abstract. Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base ” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative

M-tree: An Efficient Access Method for Similarity Search in Metric Spaces

by Paolo Ciaccia, Marco Patella, Pavel Zezula , 1997
"... A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
Abstract - Cited by 663 (38 self) - Add to MetaCart
of objects and split management, whF h keep th M-tree always balanced - severalheralvFV split alternatives are considered and experimentally evaluated. Algorithd for similarity (range and k-nearest neigh bors) queries are also described. Results from extensive experimentationwith a prototype system

Learning realistic human actions from movies

by Ivan Laptev, Marcin Marszałek, Cordelia Schmid, Benjamin Rozenfeld - IN: CVPR. , 2008
"... The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribut ..."
Abstract - Cited by 738 (48 self) - Add to MetaCart
contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we

Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics

by Geir Evensen - J. Geophys. Res , 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
Abstract - Cited by 800 (23 self) - Add to MetaCart
. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter
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