Results 1  10
of
318,644
Stratified Regularity Measures with JensenShannon Divergence
"... This paper proposes a stratified regularity measure: a novel entropic measure to describe data regularity as a function of data domain stratification. JensenShannon divergence is used to compute a setsimilarity of intensity distributions derived from stratified data. We prove that derived regulari ..."
Abstract
 Add to MetaCart
This paper proposes a stratified regularity measure: a novel entropic measure to describe data regularity as a function of data domain stratification. JensenShannon divergence is used to compute a setsimilarity of intensity distributions derived from stratified data. We prove that derived
Sequence Classification in the JensenShannon Embedding
"... This paper presents a novel approach to the supervised classification of structured objects such as sequences, trees and graphs, when the input instances are characterized by probability distributions. Distances between distributions are computed via the JensenShannon (JS) divergence, which offers ..."
Abstract
 Add to MetaCart
This paper presents a novel approach to the supervised classification of structured objects such as sequences, trees and graphs, when the input instances are characterized by probability distributions. Distances between distributions are computed via the JensenShannon (JS) divergence, which offers
On the Jensen–Shannon Divergence and Variational Distance
"... Abstract—We study the distance measures between two probability distributions via two different distance metrics, a newmetric induced from Jensen–Shannon divergence, and the well known metric. We show that several important results and constructions in computational complexity under the metric carry ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Abstract—We study the distance measures between two probability distributions via two different distance metrics, a newmetric induced from Jensen–Shannon divergence, and the well known metric. We show that several important results and constructions in computational complexity under the metric
Understanding Normal and Impaired Word Reading: Computational Principles in QuasiRegular Domains
 PSYCHOLOGICAL REVIEW
, 1996
"... We develop a connectionist approach to processing in quasiregular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic and phono ..."
Abstract

Cited by 583 (94 self)
 Add to MetaCart
We develop a connectionist approach to processing in quasiregular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic
Nonextensive Generalizations of the JensenShannon Divergence
, 2008
"... Convexity is a key concept in information theory, namely via the many implications of Jensen’s inequality, such as the nonnegativity of the KullbackLeibler divergence (KLD). Jensen’s inequality also underlies the concept of JensenShannon divergence (JSD), which is a symmetrized and smoothed vers ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Convexity is a key concept in information theory, namely via the many implications of Jensen’s inequality, such as the nonnegativity of the KullbackLeibler divergence (KLD). Jensen’s inequality also underlies the concept of JensenShannon divergence (JSD), which is a symmetrized and smoothed
Inducing Features of Random Fields
 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 664 (14 self)
 Add to MetaCart
the KullbackLeibler 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
Multimodality Image Registration by Maximization of Mutual Information
 IEEE TRANSACTIONS ON MEDICAL IMAGING
, 1997
"... A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or in ..."
Abstract

Cited by 777 (9 self)
 Add to MetaCart
A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
Abstract

Cited by 766 (29 self)
 Add to MetaCart
propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length
Globally Consistent Range Scan Alignment for Environment Mapping
 AUTONOMOUS ROBOTS
, 1997
"... A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is alig ..."
Abstract

Cited by 536 (8 self)
 Add to MetaCart
A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data
Determining Optical Flow
 ARTIFICIAL INTELLIGENCE
, 1981
"... Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent veloc ..."
Abstract

Cited by 2379 (9 self)
 Add to MetaCart
Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent
Results 1  10
of
318,644