Results 1  10
of
3,968
Learning globallyconsistent local distance functions for shapebased image retrieval and classification
 In ICCV
, 2007
"... We address the problem of visual category recognition by learning an imagetoimage distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patchbased feat ..."
Abstract

Cited by 149 (3 self)
 Add to MetaCart
distance functions— a different parameterized function for every image of our training set—whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently
Learning with local and global consistency.
 In NIPS,
, 2003
"... Abstract We consider the general problem of learning from labeled and unlabeled data, which is often called semisupervised learning or transductive inference. A principled approach to semisupervised learning is to design a classifying function which is sufficiently smooth with respect to the intr ..."
Abstract

Cited by 673 (21 self)
 Add to MetaCart
Abstract We consider the general problem of learning from labeled and unlabeled data, which is often called semisupervised learning or transductive inference. A principled approach to semisupervised learning is to design a classifying function which is sufficiently smooth with respect
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract

Cited by 599 (51 self)
 Add to MetaCart
This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Distortion invariant object recognition in the dynamic link architecture
 IEEE TRANSACTIONS ON COMPUTERS
, 1993
"... We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture exploits correlations in the finescale temporal structure of cellular signals in order to group neurons dynamically into hig ..."
Abstract

Cited by 637 (80 self)
 Add to MetaCart
are represented by sparse graphs, whose vertices are labeled by a multiresolution description in terms of a local power spectrum, and whose edges are labeled by geometrical distance vectors. Object recognition can be formulated as elastic graph matching, which is performed here by stochastic optimization of a
Multipoint quantitativetrait linkage analysis in general pedigrees
 Am. J. Hum. Genet
, 1998
"... Multipoint linkage analysis of quantitativetrait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variancecomponent linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint i ..."
Abstract

Cited by 567 (60 self)
 Add to MetaCart
chromosome for each relative pair. We have derived correlations in IBD sharing as a function of chromosomal distance for relative pairs in general pedigrees and provide a simple framework whereby these correlations can be easily obtained for any relative pair related by a single line of descent
Similarity estimation techniques from rounding algorithms
 In Proc. of 34th STOC
, 2002
"... A locality sensitive hashing scheme is a distribution on a family F of hash functions operating on a collection of objects, such that for two objects x, y, Prh∈F[h(x) = h(y)] = sim(x,y), where sim(x,y) ∈ [0, 1] is some similarity function defined on the collection of objects. Such a scheme leads ..."
Abstract

Cited by 449 (6 self)
 Add to MetaCart
A locality sensitive hashing scheme is a distribution on a family F of hash functions operating on a collection of objects, such that for two objects x, y, Prh∈F[h(x) = h(y)] = sim(x,y), where sim(x,y) ∈ [0, 1] is some similarity function defined on the collection of objects. Such a scheme leads
Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions
 J. MOL. BIOL
, 1997
"... We explore the ability of a simple simulated annealing procedure to assemble nativelike structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear as the ..."
Abstract

Cited by 393 (70 self)
 Add to MetaCart
We explore the ability of a simple simulated annealing procedure to assemble nativelike structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear
Shape matching and object recognition using low distortion correspondence
 In CVPR
, 2005
"... We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of correspond ..."
Abstract

Cited by 419 (15 self)
 Add to MetaCart
We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity
Svmknn: Discriminative nearest neighbor classification for visual category recognition
 in CVPR
, 2006
"... We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While n ..."
Abstract

Cited by 342 (10 self)
 Add to MetaCart
machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
 Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... A measure of search difficulty, fitness distance correlation (FDC), is introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy deceptiv ..."
Abstract

Cited by 258 (5 self)
 Add to MetaCart
A measure of search difficulty, fitness distance correlation (FDC), is introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy
Results 1  10
of
3,968