summary:Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbour (NN) classification rules both to improve its accuracy (editing) and to alleviate its computational burden (condensing). Methods based on selecting/discarding prototypes and methods based on adapting prototypes have been separately introduced to deal with this problem. Different approaches to this problem are considered in this paper and their main advantages and drawbacks are pointed out along with some suggestions for their joint application in some cases
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
The main two drawbacks of nearest neighbor based classifiers are: high CPU costs when the number of ...
Combining the predictions of a set of classifiers has been shown to be an effective way to create co...
summary:Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbo...
Abstract—The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve c...
Abstract—The nearest neighbor classifier is one of the most used and well-known techniques for perfo...
Prototype selection is a research field which has been active for more than four decades. As a resul...
Abstract—The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve c...
This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). B...
The k-nearest neighbour rule is commonly considered for classification tasks given its straightforwa...
The k-nearest neighbor (k-NN) algorithm is one of the most well-known supervised classifiers due to ...
Prototype generation techniques have arisen as very competitive methods for enhancing the nearest ne...
An algorithm is proposed to prune the prototype vectors (pro-totype selection) used in a nearest nei...
The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and goo...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
The main two drawbacks of nearest neighbor based classifiers are: high CPU costs when the number of ...
Combining the predictions of a set of classifiers has been shown to be an effective way to create co...
summary:Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbo...
Abstract—The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve c...
Abstract—The nearest neighbor classifier is one of the most used and well-known techniques for perfo...
Prototype selection is a research field which has been active for more than four decades. As a resul...
Abstract—The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve c...
This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). B...
The k-nearest neighbour rule is commonly considered for classification tasks given its straightforwa...
The k-nearest neighbor (k-NN) algorithm is one of the most well-known supervised classifiers due to ...
Prototype generation techniques have arisen as very competitive methods for enhancing the nearest ne...
An algorithm is proposed to prune the prototype vectors (pro-totype selection) used in a nearest nei...
The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and goo...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
The main two drawbacks of nearest neighbor based classifiers are: high CPU costs when the number of ...
Combining the predictions of a set of classifiers has been shown to be an effective way to create co...