Add to ORKGNormally the distance function used in classification in the k-Nearest Neighbors algorithm is the euclidean distance. This distance function is simple and has been shown to work on many different datasets. We propose a approach where we use multiple distance functions, one for each class, to classify the input data. To learn multiple distance functions we propose a new distance function with two learning algorithms. We show by experiments that the distance functions that we learn yields better classification accuracy than the euclidean distance, and that multiple distance functions can classify better than one
We consider improving the performance of k-Nearest Neighbor classifiers. A reg-ularized kNN is propo...
Distance functions are an important component in many learning applications. However, the correct fu...
Abstract. The k-Nearest Neighbor is one of the simplest Machine Learning algorithms. Besides its sim...
Normally the distance function used in classification in the k-Nearest Neighbors algorithm is the eu...
Many learning algorithms rely on distance metrics to receive their input data. Research has shown th...
Abstract—Many researches have been devoted to learn a Mahalanobis distance metric, which can effecti...
Based on the analysis of conditions for a good distance function we found four rules that should be ...
We show how to learn aMahanalobis distance metric for k-nearest neigh-bor (kNN) classification by se...
This paper introduces an algorithm that uses boosting to learn a distance measure for multiclass k-n...
A distance based classification is one of the popular methods for classifying instances using a poin...
The k-nearest neighbour (k-NN) classifier is one of the oldest and most important supervised learnin...
When working with high dimensional data, it is often essential to calculate the difference or "dista...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
K-Nearest Neighbor (KNN) is a method applied in classifying objects based on learning data that is c...
The selection of a suitable distance function is fundamental to the instance-based learning algorith...
We consider improving the performance of k-Nearest Neighbor classifiers. A reg-ularized kNN is propo...
Distance functions are an important component in many learning applications. However, the correct fu...
Abstract. The k-Nearest Neighbor is one of the simplest Machine Learning algorithms. Besides its sim...
Normally the distance function used in classification in the k-Nearest Neighbors algorithm is the eu...
Many learning algorithms rely on distance metrics to receive their input data. Research has shown th...
Abstract—Many researches have been devoted to learn a Mahalanobis distance metric, which can effecti...
Based on the analysis of conditions for a good distance function we found four rules that should be ...
We show how to learn aMahanalobis distance metric for k-nearest neigh-bor (kNN) classification by se...
This paper introduces an algorithm that uses boosting to learn a distance measure for multiclass k-n...
A distance based classification is one of the popular methods for classifying instances using a poin...
The k-nearest neighbour (k-NN) classifier is one of the oldest and most important supervised learnin...
When working with high dimensional data, it is often essential to calculate the difference or "dista...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
K-Nearest Neighbor (KNN) is a method applied in classifying objects based on learning data that is c...
The selection of a suitable distance function is fundamental to the instance-based learning algorith...
We consider improving the performance of k-Nearest Neighbor classifiers. A reg-ularized kNN is propo...
Distance functions are an important component in many learning applications. However, the correct fu...
Abstract. The k-Nearest Neighbor is one of the simplest Machine Learning algorithms. Besides its sim...