Results 1 - 10 of 20,271

Surface Simplification Using Quadric Error Metrics

by Michael Garland, Paul S. Heckbert
"... Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplifi ..."
Abstract - Cited by 1174 (16 self) - Add to MetaCart
simplification algorithm which can rapidly produce high quality approximations of polygonal models. The algorithm uses iterative contractions of vertex pairs to simplify models and maintains surface error approximations using quadric matrices. By contracting arbitrary vertex pairs (not just edges), our algorithm

Greed is Good: Algorithmic Results for Sparse Approximation

by Joel A. Tropp , 2004
"... This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho’s basis pursuit (BP) paradigm can recover the optimal representa ..."
Abstract - Cited by 916 (9 self) - Add to MetaCart
is an approximation algorithm for the sparse problem over a quasi-incoherent dictionary. That is, for every input signal, OMP calculates a sparse approximant whose error is only a small factor worse than the minimal error that can be attained with the same number of terms.

A Guided Tour to Approximate String Matching

by Gonzalo Navarro - ACM COMPUTING SURVEYS , 1999
"... We survey the current techniques to cope with the problem of string matching allowing errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining t ..."
Abstract - Cited by 598 (36 self) - Add to MetaCart
We survey the current techniques to cope with the problem of string matching allowing errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

by Thomas G. Dietterich , 1998
"... This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I err ..."
Abstract - Cited by 723 (8 self) - Add to MetaCart
This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I

Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition

by Y. C. Pati, R. Rezaiifar, P. S. Krishnaprasad - in Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers , 1993
"... In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. aiEne (wa.velet) frames. We propoeea modification to the Matching Pursuit algorithm of Mallat and Zhang (199 ..."
Abstract - Cited by 637 (1 self) - Add to MetaCart
recursively. where fk is the current approximation, and Rkf the current residual (error). Using initial values ofR0f = 1, fo = 0, and k = 1, the MP algorithm is comprised of the following steps,.,.41) Compute the inner-products {(Rkf,z)}. (H) Find flki such that (III) Set, I(R*f,1:n 1+,)l asupl

Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms

by Jonathan S. Yedidia, William T. Freeman, Yair Weiss - IEEE Transactions on Information Theory , 2005
"... Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems t ..."
Abstract - Cited by 585 (13 self) - Add to MetaCart
Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems

Loopy belief propagation for approximate inference: An empirical study. In:

by Kevin P Murphy , Yair Weiss , Michael I Jordan - Proceedings of Uncertainty in AI, , 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract - Cited by 676 (15 self) - Add to MetaCart
to work well. In this paper we investigate loopy prop agation empirically under a wider range of conditions. Is there something special about the error-correcting code setting, or does loopy propagation work as an approximation scheme for a wider range of networks? ..\ x(:x).) (1) where: and: The message

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

An iterative method for the solution of the eigenvalue problem of linear differential and integral

by Cornelius Lanczos , 1950
"... The present investigation designs a systematic method for finding the latent roots and the principal axes of a matrix, without reducing the order of the matrix. It is characterized by a wide field of applicability and great accuracy, since the accumulation of rounding errors is avoided, through the ..."
Abstract - Cited by 537 (0 self) - Add to MetaCart
The present investigation designs a systematic method for finding the latent roots and the principal axes of a matrix, without reducing the order of the matrix. It is characterized by a wide field of applicability and great accuracy, since the accumulation of rounding errors is avoided, through

How much should we trust differences-in-differences estimates?

by Marianne Bertrand, Esther Duflo, Sendhil Mullainathan , 2003
"... Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on femal ..."
Abstract - Cited by 828 (1 self) - Add to MetaCart
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data
Results 1 - 10 of 20,271