4 research outputs found
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ReSCon '11, Research Student Conference: Book of Abstracts
The fourth SED Research Student Conference (ReSCon2011) was hosted over three days, 20-22 June 2011, in the Hamilton Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. which focused on current research being conducted within the School of Engineering and Design by postgraduate research students from the School. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School
Comparação de desempenho entre os modelos neurais ágeis ELM e WiSARD
Neural models are popular in machine learning. Agile neural models are a subset of this kind of models and are characterized by presenting a significantly faster training time, being applied mainly in online learning domains. Two examples of agile neural models are the Extreme Learning Machine (ELM), a single hidden layer feedforward neural network which synaptic weights do not need to be iteractively adjusted, and the Wilkes, Stonham and Aleksander Recognition Device (WiSARD), a weightless neural network model with multiple discriminators that use neurons based on RAM memory structures. In this work, a comparative study between ELM and WiSARD models is made, aiming to evaluate both models performance when applied to different datasets having different characteristics. The evaluation is made by comparing test accuracy, training and testing times metrics, as well as the amount of RAM memory consumed by the models.Modelos neurais são populares na área de aprendizado de máquina. Dentre os vários tipos de modelos desta classe, os modelos neurais ágeis se destacam por apresentarem tempo de treinamento consideravelmente inferior, sendo utilizados principalmente em domÃnios de aprendizado online. Dois exemplos deste tipo de modelo são a Extreme Learning Machine (ELM), que é uma rede neural com uma única camada oculta cujos pesos sinápticos não precisam ser ajustados, e a Wilkes, Stonham and Aleksander Recognition Device (WiSARD), um modelo de rede neural sem pesos com múltiplos discriminadores que utilizam neurônios implementados como estruturas de memória RAM. Neste trabalho, ´e realizado um estudo comparativo entre os modelos neurais ágeis ELM e WiSARD, visando avaliar o desempenho de ambos quando aplicados a diferentes conjuntos de dados com diferentes caracterÃsticas. A avaliação é feita a partir da comparação das métricas de acurácia de teste, tempos de treinamento e de teste, além do uso de memória RAM dos dois modelos
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Weightless neural networks for face recognition
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The interface with the real-world has proved to be extremely challenging throughout the past 70 years in which computer technology has been developing. The problem initially is assumed to be somewhat trivial, as humans are exceptionally skilled at interpreting real-world data, for example pictures and sounds. Traditional analytical methods have so far not provided the complete answer to what will be termed pattern recognition.
Biological inspiration has motivated pattern recognition researchers since the early days of the subject, and the idea of a neural network which has self-evolving properties has always been seen to be a potential solution to this endeavour. Unlike the development of computer technology in which successive generations of improved devices have been developed, the neural network approach has been less successful, with major setbacks occurring in its development. However, the fact that natural processing in animals and humans is a voltage-based process, devoid of software, and self-evolving, provides an on-going motivation for pattern recognition in artificial neural networks. This thesis addresses the application of weightless neural networks using a ranking pre-processor to implement general pattern recognition with specific reference to face processing. The evaluation of the system will be carried out on open source databases in order to obtain a direct comparison of the efficacy of the method, in particular considerable use will be made of the MIT-CBCL face database. The methodology is cost effective in both software and hardware forms, offers real-time video processing, and can be implemented on all computer platforms. The results of this research show significant improvements over published results, and provide a viable commercial methodology for general pattern recognition