Abstract: The objective of this study has been to explore and exploit the synergy among the Nearest Neighbour (NN) editing and condensing tools previously reported in the literature in order to facilitate the use of NN techniques in near real-time applications. The extraordinary progress in the computer field has made NN techniques, once considered impractical from a computational viewpoint, feasible for consideration in time-constrained, real-world applications. This study accordingly addresses the issue of minimising the computational resource requirements of NN techniques, memory as well as time, through the use of prototype reduction techniques such as Minimal Consistent Set (MCS) selection while preserving the performance quality throu...
The quality and size of the training data sets is a critical stage on the ability of the artificial ...
Prototype generation techniques have arisen as very competitive methods for enhancing the nearest ne...
AbstractNearest Neighbor Classifiers demand high computational resources i.e, time and memory. Reduc...
unknown element according to its known nearest neighbors’ categories. This technique is efficient in...
The nearest neighbor (NN) classifier represents one of the most popular non-parametric classificatio...
Abstract—This work has two main objectives, namely, to introduce a novel algorithm, called the Fast ...
Abstract—The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve c...
The Nearest Neighbor classifier is a popular nonparametric classification method that has been succe...
Abstract. Nearest neighbour search is one of the most simple and used technique in Pattern Recogniti...
English: Nearest neighbour search is the one of the most simple and used technique in Pattern Recogn...
Prototype selection is a research field which has been active for more than four decades. As a resul...
summary:Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbo...
The learning process consists of different steps: building a Training Set (TS), training the system,...
Repeated edited nearest neighbor using unlabeled data. Our idea relies on the fact that in many appl...
Nearest neighbor (NN) classifier is the most popular non-parametric classifier. It is a simple class...
The quality and size of the training data sets is a critical stage on the ability of the artificial ...
Prototype generation techniques have arisen as very competitive methods for enhancing the nearest ne...
AbstractNearest Neighbor Classifiers demand high computational resources i.e, time and memory. Reduc...
unknown element according to its known nearest neighbors’ categories. This technique is efficient in...
The nearest neighbor (NN) classifier represents one of the most popular non-parametric classificatio...
Abstract—This work has two main objectives, namely, to introduce a novel algorithm, called the Fast ...
Abstract—The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve c...
The Nearest Neighbor classifier is a popular nonparametric classification method that has been succe...
Abstract. Nearest neighbour search is one of the most simple and used technique in Pattern Recogniti...
English: Nearest neighbour search is the one of the most simple and used technique in Pattern Recogn...
Prototype selection is a research field which has been active for more than four decades. As a resul...
summary:Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbo...
The learning process consists of different steps: building a Training Set (TS), training the system,...
Repeated edited nearest neighbor using unlabeled data. Our idea relies on the fact that in many appl...
Nearest neighbor (NN) classifier is the most popular non-parametric classifier. It is a simple class...
The quality and size of the training data sets is a critical stage on the ability of the artificial ...
Prototype generation techniques have arisen as very competitive methods for enhancing the nearest ne...
AbstractNearest Neighbor Classifiers demand high computational resources i.e, time and memory. Reduc...