2. Character Recognition System The paper is about the problem of finding good prototypes for a condensed nearest neighbor classifiei- in a recognition system. A comparison study is done between two prototype representation schemes. The prototype search is done by a genetic algorithm which is able to generate novel prototypes (i.e. prototypes which are not among the training samples). I t is shown that the generalized representation scheme is more powerful, giving significantly larger normalized interclass distances. It is also shown that both representation schemes with genetic algorithm give significantly better prototypes than a direct prototype selection algorithm, which can select only among the training samples. 1
A new evolutionary algorithm to design nearest neightbour classifiers is presented in this paper. M...
The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and goo...
This paper presents experiments of Nearest Neighbor (NN) classifier design using differ-ent evolutio...
Abstract—The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve c...
The main two drawbacks of nearest neighbor based classifiers are: high CPU costs when the number of ...
Abstract—The nearest neighbor classifier is one of the most used and well-known techniques for perfo...
The nearest neighbor (NN) approach is a powerful nonparametric technique for pattern classification ...
In pattern classification problems, many works have been carried out with the aim of designing good ...
This paper discusses a genetic-algorithm-based approach for selecting a small number of representati...
Prototype selection is a research field which has been active for more than four decades. As a resul...
Prototype generation techniques have arisen as very competitive methods for enhancing the nearest ne...
The design of nearest neighbour classifiers can be seen as the partitioning of the whole domain in d...
Combining the predictions of a set of classifiers has been shown to be an effective way to create co...
A nearest-neighbor classifier compares an unclassified object to a set of preclassified examples and...
Abstract—Dissimilarities can be a powerful way to represent objects like strings, graphs and images ...
A new evolutionary algorithm to design nearest neightbour classifiers is presented in this paper. M...
The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and goo...
This paper presents experiments of Nearest Neighbor (NN) classifier design using differ-ent evolutio...
Abstract—The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve c...
The main two drawbacks of nearest neighbor based classifiers are: high CPU costs when the number of ...
Abstract—The nearest neighbor classifier is one of the most used and well-known techniques for perfo...
The nearest neighbor (NN) approach is a powerful nonparametric technique for pattern classification ...
In pattern classification problems, many works have been carried out with the aim of designing good ...
This paper discusses a genetic-algorithm-based approach for selecting a small number of representati...
Prototype selection is a research field which has been active for more than four decades. As a resul...
Prototype generation techniques have arisen as very competitive methods for enhancing the nearest ne...
The design of nearest neighbour classifiers can be seen as the partitioning of the whole domain in d...
Combining the predictions of a set of classifiers has been shown to be an effective way to create co...
A nearest-neighbor classifier compares an unclassified object to a set of preclassified examples and...
Abstract—Dissimilarities can be a powerful way to represent objects like strings, graphs and images ...
A new evolutionary algorithm to design nearest neightbour classifiers is presented in this paper. M...
The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and goo...
This paper presents experiments of Nearest Neighbor (NN) classifier design using differ-ent evolutio...