Emprego de teoria de agentes no desenvolvimento de dispositivos neurocomputacionais hibridos e aplicação ao controle e identificação de sistemas dinamicos

Orientador : Fernando Jose Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad

Committee machines: a unified approach using support vector machines

Orientador : Fernando Jose Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Os algoritmos baseados em métodos de kernel destacam-se entre as diversas técnicas de aprendizado de máquina. Eles foram inicialmente empregados na implementação de máquinas de vetores-suporte (SVMs). A abordagem SVM representa um procedimento de aprendizado não-paramétrico para classificação e regressão de alto desempenho. No entanto, existem aspectos estruturais e paramétricos de projeto que podem conduzir a uma degradação de desempenho. Na ausência de uma metodologia sistemática e de baixo custo para a proposição de modelos computacionais otimamente especificados, os comitês de máquinas se apresentam como alternativas promissoras. Existem versões estáticas de comitês, na forma de ensembles de componentes, e versões dinâmicas, na forma de misturas de especialistas. Neste estudo, os componentes de um ensemble e os especialistas de uma mistura são tomados como SVMs. O objetivo é explorar conjuntamente potencialidades advindas de SVM e comitê de máquinas, adotando uma formulação unificada. Várias extensões e novas configurações de comitês de máquinas são propostas, com análises comparativas que indicam ganho significativo de desempenho frente a outras propostas de aprendizado de máquina comumente adotadas para classificação e regressãoAbstract: Algorithms based on kernel methods are prominent techniques among the available approaches for machine learning. They were initially applied to implement support vector machines (SVMs). The SVM approach represents a nonparametric learning procedure devoted to high performance classification and regression tasks. However, structural and parametric aspects of the design may guide to performance degradation. In the absence of a systematic and low-cost methodology for the proposition of optimally specified computational models, committee machines emerge as promising alternatives. There exist static versions of committees, in the form of ensembles of components, and dynamic versions, in the form of mixtures of experts. In the present investigation, the components of an ensemble and the experts of a mixture are taken as SVMs. The aim is to jointly explore the potentialities of both SVM and committee machine, by means of a unified formulation. Several extensions and new configurations of committee machines are proposed, with comparative analyses that indicate significant gain in performance before other proposals for machine learning commonly adopted for classification and regressionDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Automatic segmentation of grammatical facial expressions in sign language: towards an inclusive communication experience

Nowadays, natural language processing techniques enable the development of applications that promote communication between humans and between humans and machines. Although the technology related to automated oral communication is mature and affordable, there are currently no appropriate solutions for visual-spatial languages. In the scarce efforts to automatically process sign languages, studies on non-manual gestures are rare, making it difficult to properly interpret the speeches uttered in those languages. In this paper, we present a solution for the automatic segmentation of grammatical facial expressions in sign language. This is a low-cost computational solution designed to integrate a sign language processing framework that supports the development of simple but high value-added applications for the context of universal communication. Moreover, we present a discussion of the difficulties faced by this solution to guide future research in this area

Técnicas Baseadas em Subespaço para Reconstrução de Faces Parcialmente Ocluídas: Um Estudo Comparativo

Os sistemas de reconhecimento facial em ambientes não controlados precisam lidar com variações de iluminação, pose, expressão e oclusão, as quais introduzem variações intraclasse e degradam a performance de reconhecimento, diferentemente dos ambientes controlados que apresentam resultados satisfatórios. Comparada com problemas de pose, iluminação e expressão, o problema relacionado à oclusão é relativamente pouco estudado na área. Na literatura existem algumas técnicas baseadas em PCA que tem sido aplicadas  ao problema de oclusão. No entanto, não existe um estudo apresentando os prós  e contras de cada variação. Este trabalho visa apresentar um estudo comparativo envolvendo técnicas baseadas em subespaço para a tarefa de reconstrução de faces parcialmente ocluídas

Machine Learning in Textual Content-Based Recommendation Systems: A Systematic Review

Content-based Recommendation Systems (CbRS) is a research area in which Machine Learning (ML) strategies can be applied with success. However, specifically in textual CbRS, the use of ML has not been expressive in recent years. To contribute to the evolution of the intersection of such areas, we present a Systematic Review to identify, interpret and evaluate how the ML strategies have been applied to CbRS

Gesture unit segmentation using support vector machines: segmenting gestures from rest positions

Gesture analysis has been widely used for developing new\ud methods of human-computer interaction. The advancement\ud reached in the gesture analysis area is also motivating its\ud application to automate tasks related to discourse analysis,\ud such as the gesture phases segmentation task. In this paper,\ud we present an initiative that aims at segmenting gestures,\ud especially considering the \units" { the larger grain involved\ud in gesture phases segmentation. Thereunto, we have captured\ud the gestures using a Xbox KinectTMdevice, modeled\ud the problem as a classi cation task, and applied Support\ud Vector Machines. Moreover, aiming at taking advantage\ud from the temporal aspects involved in the problem, we have\ud used several types of data pre-processing in order to consider\ud time domain and frequency domain featuresThe first author thanks São Paulo Research Foundation (FAPESP/Brazil) - process number 2011/04608-

A comparative study of feature level fusion strategies for Multimodal Biometric Systems based on Face and Iris

With the technology advances, new approaches for automatic recognition of a person’s identity have been proposed and such a fact has encouraged the use of Biometrics Systems. This approach uses physical or behavioural characteristics of the user in order to recognize or authenticate their identity. The Biometric Systems can be classified as Unimodal or Multimodal. The Unimodal Systems use a single biometric modality to perform the recognition, while the Multimodal ones use two or more modalities. A Multimodal Biometric System can be constructed in different ways, according to its architecture, fusion level and fusion strategies. The main of this work is to investigate and compare different feature level fusion strategies, in order to design a Multimodal Biometric System with high performance. In this paper, we used the discrete wavelet transform to extract the feature sets from iris and face images. Experimental results show that Multimodal Biometric Systems outperform Unimodal Biometric Systems according to recognition rate computed over the outputs produced by the induced Support Vector Machine classifier