Auto-associative memory using n-tuple techniques

The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.

Inference From Aging Information

For many learning tasks the duration of the data collection can be greater than the time scale for changes of the underlying data distribution. The question we ask is how to include the information that data are aging. Ad hoc methods to achieve this include the use of validity windows that prevent the learning machine from making inferences based on old data. This introduces the problem of how to define the size of validity windows. In this brief, a new adaptive Bayesian inspired algorithm is presented for learning drifting concepts. It uses the analogy of validity windows in an adaptive Bayesian way to incorporate changes in the data distribution over time. We apply a theoretical approach based on information geometry to the classification problem and measure its performance in simulations. The uncertainty about the appropriate size of the memory windows is dealt with in a Bayesian manner by integrating over the distribution of the adaptive window size. Thus, the posterior distribution of the weights may develop algebraic tails. The learning algorithm results from tracking the mean and variance of the posterior distribution of the weights. It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps.

Implementação e avaliação de máquinas de comitê em um ambiente com múltiplos processadores embarcados em um único chip

The number of applications based on embedded systems grows significantly every year, even with the fact that embedded systems have restrictions, and simple processing units, the performance of these has improved every day. However the complexity of applications also increase, a better performance will always be necessary. So even such advances, there are cases, which an embedded system with a single unit of processing is not sufficient to achieve the information processing in real time. To improve the performance of these systems, an implementation with parallel processing can be used in more complex applications that require high performance. The idea is to move beyond applications that already use embedded systems, exploring the use of a set of units processing working together to implement an intelligent algorithm. The number of existing works in the areas of parallel processing, systems intelligent and embedded systems is wide. However works that link these three areas to solve any problem are reduced. In this context, this work aimed to use tools available for FPGA architectures, to develop a platform with multiple processors to use in pattern classification with artificial neural networks

Um sistema inteligente de classificação de sinais de EEG para Interface Cérebro-Computador

The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature

Classificação de padrões através de um comitê de máquinas aprimorado por aprendizagem por reforço

Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Classificador neural híbrido para imagens obtidas por sensoriamento remoto

Remote sensing is one technology of extreme importance, allowing capture of data from the Earth's surface that are used with various purposes, including, environmental monitoring, tracking usage of natural resources, geological prospecting and monitoring of disasters. One of the main applications of remote sensing is the generation of thematic maps and subsequent survey of areas from images generated by orbital or sub-orbital sensors. Pattern classification methods are used in the implementation of computational routines to automate this activity. Artificial neural networks present themselves as viable alternatives to traditional statistical classifiers, mainly for applications whose data show high dimensionality as those from hyperspectral sensors. This work main goal is to develop a classiffier based on neural networks radial basis function and Growing Neural Gas, which presents some advantages over using individual neural networks. The main idea is to use Growing Neural Gas's incremental characteristics to determine the radial basis function network's quantity and choice of centers in order to obtain a highly effective classiffier. To demonstrate the performance of the classiffier three studies case are presented along with the results.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning

In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.

Avaliação do uso da terra em micro̧bacia utilizando uma matriz de partição fuzzy

Informatics evolution presently offers the possibility of new technique and methodology development for studies in all human knowledge areas. In addition, the present personal computer capacity of handling a large volume of data makes the creation and application of new analysis tools easy. This paper aimed the application of a fuzzy partition matrix to analyze data obtained from the Landsat 5 TMN sensor, in order to elaborate the supervised classification of land use in Arroio das Pombas microbasin in Botucatu, SP, Brazil. It was possible that one single training area present input in more than one covering class due to weight attribution at the signature creation moment. A change in the classification result was also observed when compared to maximum likelihood classification, mainly when related to bigger uniformity and better class edges classification.

Binary Bat Algorithm for Feature Selection

Feature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.

Identificação de fácies em perfis de poço com algoritmo inteligente

A identificação de fácies em um poço não testemunhado é um dos problemas clássicos da avaliação de formação. Neste trabalho este problema é tratado em dois passos, no primeiro produz-se a codificação da informação geológica ou da descrição das fácies atravessadas em um poço testemunhado em termos das suas propriedades físicas registradas nos perfis geofísicos e traduzidas pelos parâmetros L e K, que são obtidos a partir dos perfis de porosidade (densidade, sônico e porosidade neutrônica) e pela argilosidade (Vsh) calculada pelo perfil de raio gama natural. Estes três parâmetros são convenientemente representados na forma do Gráfico Vsh-L-K. No segundo passo é realizada a interpretação computacional do Gráfico Vsh-L-K por um algoritmo inteligente construído com base na rede neural competitiva angular generalizada, que é especializada na classificação de padrões angulares ou agrupamento de pontos no espaço n-dimensional que possuem uma envoltória aproximadamente elipsoidal. Os parâmetros operacionais do algoritmo inteligente, como a arquitetura da rede neural e pesos sinápticos são obtidos em um Gráfico Vsh-L-K, construído e interpretado com as informações de um poço testemunhado. Assim, a aplicação deste algoritmo inteligente é capaz de identificar e classificar as camadas presentes em um poço não testemunhado, em termos das fácies identificadas no poço testemunhado ou em termos do mineral principal, quando ausentes no poço testemunhado. Esta metodologia é apresentada com dados sintéticos e com perfis de poços testemunhados do Campo de Namorado, na Bacia de Campos, localizada na plataforma continental do Rio de Janeiro, Brasil.

A Dataset for Evaluating Intrusion Detection Systems in IEEE 802.11 Wireless Networks

Internet access by wireless networks has grown considerably in recent years. However, these networks are vulnerable to security problems, especially those related to denial of service attacks. Intrusion Detection Systems(IDS)are widely used to improve network security, but comparison among the several existing approaches is not a trivial task. This paper proposes building a datasetfor evaluating IDS in wireless environments. The data were captured in a real, operating network. We conducted tests using traditional IDS and achieved great results, which showed the effectiveness of our proposed approach.

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)