Add to ORKGA windowed version of the Nearest Neighbour (WNN) classifier for images is described. While its construction is inspired by the architecture of Artificial Neural Networks, the underlying theoretical framework is based on approximation theory. We illustrate WNN on the datasets MNIST and EMNIST of images of handwritten digits. In order to calibrate the parameters of WNN, we first study it on the classical MNIST dataset. We then apply WNN with these parameters to the challenging EMNIST dataset. It is demonstrated that WNN misclassifies 0.42% of the images of EMNIST and therefore significantly outperforms predictions by humans and shallow ANNs that both have more than 1.3% of errors
Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectivenes...
We propose a new architecture for difficult image processing operations, such as natural edge detect...
In many application areas of machine learning, prior knowledge concerning the monotonicity of relati...
We suggest a novel classification algorithm that is based on local approximations and explain its co...
In this paper, we suggest basing the development of classification methods on traditional techniques...
The performance of a state-of-the-art neural network classifier for hand-written digits is compared ...
under revision for IJCVInternational audienceThe k-nearest neighbors (k-NN) classification rule has ...
International audienceNaive Bayes Nearest Neighbor (NBNN) is a feature-based image classifier that a...
High feature dimensionality of realistic datasets adversely affects the recognition accuracy of near...
Parametric image classification methods are usually complex because they require intensive training....
In this paper, we propose a coarse to fine K nearest neighbor (KNN) classifier (CFKNNC). CFKNNC diff...
Abstract. Naive Bayes Nearest Neighbor (NBNN) has been proposed as a powerful, learning-free, non-pa...
International audienceThe purpose of this paper is to compare two pattern recognition methods : Neur...
In this paper, we describe our experiments on training a computer to recognize (classify) a black an...
Abstract---We propose a simple kernel based nearest neighbor approach for handwritten digit classifi...
Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectivenes...
We propose a new architecture for difficult image processing operations, such as natural edge detect...
In many application areas of machine learning, prior knowledge concerning the monotonicity of relati...
We suggest a novel classification algorithm that is based on local approximations and explain its co...
In this paper, we suggest basing the development of classification methods on traditional techniques...
The performance of a state-of-the-art neural network classifier for hand-written digits is compared ...
under revision for IJCVInternational audienceThe k-nearest neighbors (k-NN) classification rule has ...
International audienceNaive Bayes Nearest Neighbor (NBNN) is a feature-based image classifier that a...
High feature dimensionality of realistic datasets adversely affects the recognition accuracy of near...
Parametric image classification methods are usually complex because they require intensive training....
In this paper, we propose a coarse to fine K nearest neighbor (KNN) classifier (CFKNNC). CFKNNC diff...
Abstract. Naive Bayes Nearest Neighbor (NBNN) has been proposed as a powerful, learning-free, non-pa...
International audienceThe purpose of this paper is to compare two pattern recognition methods : Neur...
In this paper, we describe our experiments on training a computer to recognize (classify) a black an...
Abstract---We propose a simple kernel based nearest neighbor approach for handwritten digit classifi...
Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectivenes...
We propose a new architecture for difficult image processing operations, such as natural edge detect...
In many application areas of machine learning, prior knowledge concerning the monotonicity of relati...