670 research outputs found
Bus line trajectories classification using weightless neural networks
Geo-enabled devices are ubiquitous nowadays. Within a diversity of possible applications using the huge of amount data generated by this technology, our work focuses on a chronic problem of Rio de Janeiro city: its public bus system. This text presents a framework for GPS trajectories classification, whose focus is the identification of bus routes of a public bus system. In order to do that, it was used the lightweight and versatile WiSARD, a weightless neural network classifier. Different binarization methods were used to adapt raw data to WiSARD’s binary input, making use of a set of rules defined by the application domain. Yet, it is evaluated a way of combining WiSARD through decision directed acyclic graphs. All these approachs result in different flavors of a neuro-symbolic learning system. The framework was tested against a vast data set created from open access and real-time data acquired from the current bus system of Rio de Janeiro city. Results obtained suggest the applicability of the proposed solution in a classification problem with more than 500 classes. Comparisons made also indicate an equivalent performance of WiSARD and other state-of-art and widely used machine learning methods. In addition, the framework described here is believed to be adaptable to other application domains.Dispositivos com localização espacial estĂŁo em toda parte hoje em dia. Dentre várias possĂveis aplicações com a grande quantidade de dados gerada por esse tipo de equipamento, nosso trabalho foca em um problema crĂ´nico da cidade do Rio de Janeiro: seu sistema pĂşblico de Ă´nibus. Apresenta-se neste texto uma arquitetura para classificação de trajetĂłrias GPS, cujo foco Ă© a identificação de rotas de Ă´nibus do sistema pĂşblico. Para isso, utilizamos o leve e versátil classificador baseado em redes neurais sem peso WiSARD. Para a geração da entrada da rede, experimentamos diferentes formas de binarização, fazendo uso de regras definidas pelo problema. Ainda, avaliamos uma forma de combinação das redes WiSARD com o uso de um grafo acĂclico de decisões. Todas essas propostas resultam em diferentes sabores de um sistema de aprendizado neurossimbĂłlico. Tal arquitetura foi testada contra um vasto conjunto de dados construĂdo a partir de dados fornecido em tempo real e de forma pĂşblica pelo sistema corrente da cidade do Rio de Janeiro. Os resultados obtidos indicam a aplicabilidade da solução proposta em um problema de classificação envolvendo mais de 500 classes. As comparações efetuadas indicam uma equiparação do modelo WiSARD com outros modelos em estado da arte. No mais, acreditamos que a metodologia aqui descrita possa ser utilizada com sucesso em outros domĂnios
Rejection-oriented learning without complete class information
Machine Learning is commonly used to support decision-making in numerous, diverse contexts. Its usefulness in this regard is unquestionable: there are complex systems built on the top of machine learning techniques whose descriptive and predictive capabilities go far beyond those of human beings. However, these systems still have limitations, whose analysis enable to estimate their applicability and confidence in various cases. This is interesting considering that abstention from the provision of a response is preferable to make a mistake in doing so. In the context of classification-like tasks, the indication of such inconclusive output is called rejection. The research which culminated in this thesis led to the conception, implementation and evaluation of rejection-oriented learning systems for two distinct tasks: open set recognition and data stream clustering. These system were derived from WiSARD artificial neural network, which had rejection modelling incorporated into its functioning. This text details and discuss such realizations. It also presents experimental results which allow assess the scientific and practical importance of the proposed state-of-the-art methodology.Aprendizado de Máquina Ă© comumente usado para apoiar a tomada de decisĂŁo em numerosos e diversos contextos. Sua utilidade neste sentido Ă© inquestionável: existem sistemas complexos baseados em tĂ©cnicas de aprendizado de máquina cujas capacidades descritivas e preditivas vĂŁo muito alĂ©m das dos seres humanos. Contudo, esses sistemas ainda possuem limitações, cuja análise permite estimar sua aplicabilidade e confiança em vários casos. Isto Ă© interessante considerando que a abstenção da provisĂŁo de uma resposta Ă© preferĂvel a cometer um equĂvoco ao realizar tal ação. No contexto de classificação e tarefas similares, a indicação desse resultado inconclusivo Ă© chamada de rejeição. A pesquisa que culminou nesta tese proporcionou a concepção, implementação e avaliação de sistemas de aprendizado orientados `a rejeição para duas tarefas distintas: reconhecimento em cenário abertos e agrupamento de dados em fluxo contĂnuo. Estes sistemas foram derivados da rede neural artificial WiSARD, que teve a modelagem de rejeição incorporada a seu funcionamento. Este texto detalha e discute tais realizações. Ele tambĂ©m apresenta resultados experimentais que permitem avaliar a importância cientĂfica e prática da metodologia de ponta proposta
Design for novel enhanced weightless neural network and multi-classifier.
Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems.
A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN.
Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems
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