Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
792
Using Mahalanobis distance to compare
, 2008
"... genomic signatures between bacterial plasmids and chromosomes ..."
An alternative to the Mahalanobis distance for determining
"... optimal correspondences in data association ..."
On the Mahalanobis-distance based penalized
"... empirical likelihood method in high dimensions ..."
Classification with Kernel Mahalanobis Distance Classifiers
"... Abstract. Within the framework of kernel methods, linear data methods have almost completely been extended to their nonlinear counterparts. In this paper, we focus on nonlinear kernel techniques based on the Mahalanobis distance. Two approaches are distinguished here. The first one assumes an invert ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
Abstract. Within the framework of kernel methods, linear data methods have almost completely been extended to their nonlinear counterparts. In this paper, we focus on nonlinear kernel techniques based on the Mahalanobis distance. Two approaches are distinguished here. The first one assumes
On kernelization of supervised mahalanobis distance learners
- Computing Research Repoisitory (CoRR
"... Abstract. This paper contains three contributions to the problem of learning a Mahalanobis distance. First, a general framework for kernelizing Mahalanobis distance learners is presented. The framework allows existing algorithms to learn a Mahalanobis distance in a feature space associated with a pr ..."
Abstract
-
Cited by 8 (0 self)
- Add to MetaCart
Abstract. This paper contains three contributions to the problem of learning a Mahalanobis distance. First, a general framework for kernelizing Mahalanobis distance learners is presented. The framework allows existing algorithms to learn a Mahalanobis distance in a feature space associated with a
On Kernelizing Mahalanobis Distance Learning Algorithms On Kernelizing Mahalanobis Distance Learning Algorithms
, 804
"... This paper focuses on the problem of kernelizing an existing supervised Mahalanobis distance learner. The following features are included in the paper. Firstly, three popular learners, namely, “neighborhood component analysis”, “large margin nearest neighbors” and “discriminant neighborhood embeddin ..."
Abstract
- Add to MetaCart
This paper focuses on the problem of kernelizing an existing supervised Mahalanobis distance learner. The following features are included in the paper. Firstly, three popular learners, namely, “neighborhood component analysis”, “large margin nearest neighbors” and “discriminant neighborhood
Combining Euclidean and Mahalanobis Distances for Recognition
"... We consider recognition by Gaussian models, noise sensitivity from using sample covariances to estimate population covariances, and correction by combining Euclidean and Mahalanobis distances. 1 Introduction The Zernike moments uniquely describe functions on the unit disk, and can be extended to im ..."
Abstract
- Add to MetaCart
We consider recognition by Gaussian models, noise sensitivity from using sample covariances to estimate population covariances, and correction by combining Euclidean and Mahalanobis distances. 1 Introduction The Zernike moments uniquely describe functions on the unit disk, and can be extended
Learning Local Invariant Mahalanobis Distances
"... Abstract For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many ma ..."
Abstract
- Add to MetaCart
machine learning applications, and tolerance for image transformations was primarily achieved by using robust feature vectors. In this paper we propose a novel and computationally efficient way to learn a local Mahalanobis metric per datum, and show how we can learn a local invariant metric to any
Image Segmentation By Self Organizing Map With Mahalanobis Distance
"... Abstract — Image segmentation is the classification of data sets into group of similar data points. This article proposed a method to determine the winner unit by self organizing mapping network. The distance between the input vector and the weight vector has been determined by mahalanobis distance ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
Abstract — Image segmentation is the classification of data sets into group of similar data points. This article proposed a method to determine the winner unit by self organizing mapping network. The distance between the input vector and the weight vector has been determined by mahalanobis distance
Results 1 - 10
of
792
