From Data Topology to a Modular Classifier

This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given

A K Nearest Classifier design

This paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results are given for synthetic problems, for an image segmentation problem from the UCI repository and for a handwritten digit recognition problem

A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine

Machine learning methods are used today for most recognition problems. Convolutional Neural Networks (CNN) have time and again proved successful for many image processing tasks primarily for their architecture. In this paper we propose to apply CNN to small data sets like for example, personal albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model. We use Artificial Immune System Principles to enhance small size of training data set. A layer of Clonal Selection is added to the local filtering and max pooling of CNN Architecture. The proposed Architecture is evaluated using the standard MNIST dataset by limiting the data size and also with a small personal data sample belonging to two different classes. Experimental results show that the proposed hybrid CNN-AIS based recognition engine works well when the size of training data is limited in siz

Detection of ambiguous patterns in a SOM based recognition system: application to handwritten numeral classification

This work presents a system for pattern recognition that combines a self-organising unsupervised technique (via a Kohonen-type SOM) with a bayesian strategy in order to classify input patterns from a given probability distribution and, at the same time, detect ambiguous cases and explain answers. We apply the system to the recognition of handwritten digits. This proposal is intended as an improvement of a model previously introduced by our group, consisting basically of a hybrid unsupervised, self-organising model, followed by a supervised stage. Experiments were carried out on the handwritten digit database of the Concordia University, which is generally accepted as one of the standards in most of the literature in the field

Integration of traditional imaging, expert systems, and neural network techniques for enhanced recognition of handwritten information

Includes bibliographical references (p. 33-37).Research supported by the I.F.S.R.C. at M.I.T.Amar Gupta, John Riordan, Evelyn Roman

Skeletonization of sparse shapes using dynamic competitive neural networks

La detección de regiones y objetos en imágenes digitales es un tema de suma importancia en la resolución de numerosos problemas correspondientes al área de reconocimiento de patrones. En esta dirección los algoritmos de esqueletización son una herramienta muy utilizada ya que permiten reducir la cantidad de información disponible facilitando la extracción de características para su posterior reconocimiento y clasificación. Además, esta transformación de la información original en sus características esenciales, facilita la eliminación de ruidos locales presentes en la entrada de datos. Este artículo propone una nueva estrategia de esqueletización aplicable a imágenes esparcidas a partir de una red neuronal competitiva dinámica entrenada con el método AVGSOM. La estrategia desarrollada en este trabajo determina los arcos que forman el esqueleto combinando el aprendizaje no supervisado del AVGSOM con un árbol de dispersión mínima (minimun spaning tree). El método propuesto ha sido aplicado en imágenes con diferente forma y grado de dispersión. En particular, los resultados obtenidos han sido comparados con soluciones existentes mostrando resultados satisfactorios. Finalmente se presentan algunas conclusiones así como algunas líneas de trabajo futurasThe detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and classification. In addition, this transformation of the original data in its essential characteristics eases the elimination of local noise which is present in the data input. This paper proposes a new skeletonization strategy applicable to sparse images from a competitive, dynamic neural network trained with the AVGSOM method. The strategy developed in this paper determines the arc making up the skeleton combining AVGSOM non-supervised learning with a minimum spanning tree. The proposed method has been applied in images with different spanning shape and degree. In particular, the results obtained have been compared to existing solutions, showing successful results. Finally, some conclusions, together with some future lines of work, are presented.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition

Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline Gurmukhi handwritten character and numeral recognition (HCNR). The proposed network works efficiently for training as well as testing and exhibits a good recognition performance. Two primary datasets comprising of offline handwritten Gurmukhi characters and Gurmukhi numerals have been employed in the present work. The testing accuracies achieved using the proposed network is 98.5% for characters and 98.6% for numerals

A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering [QP363.3. T253 2006 f rb].

Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi, serupa dengan penerangan teori bagi peta otak, untuk visualisasi, klasifikasi dan pengklusteran data. In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving map, which is akin to the theoretical explanation of the brain map, for data visualisation, classification, and clustering is presented

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225