Results 11  20
of
1,803
COVERAGE AND REDUNDANCY
"... Test data should cover the domain and criticalities without including too much redundancy so that test efforts are acceptable. The similarity of images can be defined in many ways but when sticking to local pixel features [7], one can easily get unwanted results. We use low discrepancy sampling tech ..."
Abstract
 Add to MetaCart
Test data should cover the domain and criticalities without including too much redundancy so that test efforts are acceptable. The similarity of images can be defined in many ways but when sticking to local pixel features [7], one can easily get unwanted results. We use low discrepancy sampling
On the stopping distance and the stopping redundancy of codes
 IEEE Trans. Inf. Theory
, 2006
"... Abstract — It is now well known that the performance of a linear code C under iterative decoding on a binary erasure channel (and other channels) is determined by the size of the smallest stopping set in the Tanner graph for C. Several recent papers refer to this parameter as the stopping distance s ..."
Abstract

Cited by 56 (2 self)
 Add to MetaCart
check matrix for C (with sufficiently many dependent rows) such that s = d. We thus introduce a new parameter, termed the stopping redundancy of C, defined as the minimum number of rows in a paritycheck matrix H for C such that the corresponding stopping distance s(H) attains its largest possible value
RealTime Inverse Kinematics Techniques for Anthropomorphic Limbs
 Graphical Models
, 2000
"... this paper we develop a set of inverse kinematics algorithms suitable for an anthropomorphic arm or leg. We use a combination of analytical and numerical methods to solve generalized inverse kinematics problems including position, orientation, and aiming constraints. Our combination of analytical ..."
Abstract

Cited by 180 (3 self)
 Add to MetaCart
of parameters that define the redundancy of the system. c 2000 Academic Press Key Words: inverse kinematics; realtime IK; human arm kinematics; analytical algorithms
1norm Support Vector Machines
 Neural Information Processing Systems
, 2003
"... The standard 2norm SVM is known for its good performance in twoclass classification. In this paper, we consider the 1norm SVM. We argue that the 1norm SVM may have some advantage over the standard 2norm SVM, especially when there are redundant noise features. We also propose an efficient alg ..."
Abstract

Cited by 174 (15 self)
 Add to MetaCart
The standard 2norm SVM is known for its good performance in twoclass classification. In this paper, we consider the 1norm SVM. We argue that the 1norm SVM may have some advantage over the standard 2norm SVM, especially when there are redundant noise features. We also propose an efficient
2007a) Redundant overdispersion parameters in multilevel models
 J. Educ. Behav. Statist
"... In some distributions, such as the binomial distribution, the variance is determined by the mean. However, in practice, overdispersion is often observed where the variance is larger than that predicated by the mean, and underdispersion is sometimes observed where the variance is smaller. It is wel ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
. It is well known that overdispersion or underdispersion cannot be modeled for dichotomous responses having a Bernoulli distribution. Redundant overdispersion parameters are nevertheless often included when multilevel or hierarchical models for categorical responses are estimated using quasi
Parameter Redundancy in Multistate CaptureRecapture Models
"... Multistate capturerecapture models are a powerful tool to address a variety of biological questions concerning dispersal and/or individual variability in wild animal populations. However, biologically meaningful models are often overparameterized and consequently some parameters cannot be estimat ..."
Abstract
 Add to MetaCart
mated separately. Identifying which quantities are separately estimable is crucial for proper model selection based upon likelihood tests or information criteria and for the interpretation of the estimates obtained. We show how to investigate parameter redundancy in multistate capturerecapture models, based
A Note On ARMA Model Parameter Redundancy
, 1991
"... A simple condition, which is expressed directly in terms of the ARMA model parameters, is given for determining ARMA model redundancy. Key words and phrases: Auxiliary matrix, Mathematica, model redundancy � roots of polynomial equations. A.I. MCLEOD 3 The stationary and invertible ARMA(p � q) mode ..."
Abstract
 Add to MetaCart
A simple condition, which is expressed directly in terms of the ARMA model parameters, is given for determining ARMA model redundancy. Key words and phrases: Auxiliary matrix, Mathematica, model redundancy � roots of polynomial equations. A.I. MCLEOD 3 The stationary and invertible ARMA(p � q
Fast Discrete Curvelet Transforms
, 2005
"... This paper describes two digital implementations of a new mathematical transform, namely, the second generation curvelet transform [12, 10] in two and three dimensions. The first digital transformation is based on unequallyspaced fast Fourier transforms (USFFT) while the second is based on the wrap ..."
Abstract

Cited by 175 (9 self)
 Add to MetaCart
on the wrapping of specially selected Fourier samples. The two implementations essentially differ by the choice of spatial grid used to translate curvelets at each scale and angle. Both digital transformations return a table of digital curvelet coefficients indexed by a scale parameter, an orientation parameter
Printed in Great Britain Detecting parameter redundancy
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
Abstract
 Add to MetaCart
All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Image denoising with shrinkage and redundant representations
 IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2006
"... Shrinkage is a well known and appealing denoising technique. The use of shrinkage is known to be optimal for Gaussian white noise, provided that the sparsity on the signal’s representation is enforced using a unitary transform. Still, shrinkage is also practiced successfully with nonunitary, and eve ..."
Abstract

Cited by 38 (4 self)
 Add to MetaCart
, and even redundant representations. In this paper we shed some light on this behavior. We show that simple shrinkage could be interpreted as the first iteration of an algorithm that solves the basis pursuit denoising (BPDN) problem. Thus, this work leads to a novel iterative shrinkage algorithm that can
Results 11  20
of
1,803