Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

A new Pennsylvanian Oriocrassatellinae from Brazil and the distribution of the genus Oriocrassatella in space and time

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Teaching real-time with a scheduler simulator

In this paper we describe a scheduler simulator for real-time tasks, RTsim, that can be used as a tool to teach real-time scheduling algorithms. It simulates a variety of preprogrammed scheduling policies for single and multi-processor systems and simple algorithm variants introduced by its user. Using RTsim students can conduct experiments that will allow them to understand the effects of each policy given different load conditions and learn which policy is better for different workloads. We show how to use RTsim as a learning tool and the results achieved with its application on the Real-Time Systems course taught at the B.Sc. on Computer Science at Paulista State University - Unesp - at Rio Preto.

A framework for learning in humanoid simulated robots

One of the most important characteristics of intelligent activity is the ability to change behaviour according to many forms of feedback. Through learning an agent can interact with its environment to improve its performance over time. However, most of the techniques known that involves learning are time expensive, i.e., once the agent is supposed to learn over time by experimentation, the task has to be executed many times. Hence, high fidelity simulators can save a lot of time. In this context, this paper describes the framework designed to allow a team of real RoboNova-I humanoids robots to be simulated under USARSim environment. Details about the complete process of modeling and programming the robot are given, as well as the learning methodology proposed to improve robot's performance. Due to the use of a high fidelity model, the learning algorithms can be widely explored in simulation before adapted to real robots. © 2008 Springer-Verlag Berlin Heidelberg.

Structural change and productivity growth, 20 years later: old problems, new opportunities

Includes bibliography

The contribution of biofuels to the sustainability of development in Latin America and the Caribbean

Includes bibliography

Automatic code generation for microcontrollers from place-transition Petri net models

This paper describes a program for the automatic generation of code for Intel's 8051 microcontroller. The code is generated from a place-transition Petri net specification. Our goal is to minimize programming time. The code generated by our program has been observed to exactly match the net model. It has also been observed that no change is needed to be made to the generated code for its compilation to the target architecture. © 2011 IFAC.

Structural change and productivity growth 20 years later: old problems, new opportunities. Summary

Includes bibliography

Adote o artista não deixe ele virar professor: reflexões em torno do híbrido professor performer

Pós-graduação em Artes - IA

T-Learning: limites e possibilidades em televisão digital interativa

Pós-graduação em Televisão Digital: Informação e Conhecimento - FAAC

O processo de construção do conhecimento histórico para alunos em educação de jovens e adultos: um olhar para as práticas de leitura e escrita

Pós-graduação em Educação - IBRC

Um retrato do primeiro ano do ensino fundamental: o que revelam crianças, pais e professoras

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O ensino de ciências nas séries iniciais do ensino fundamental: compreendendo discursos de professoras-alunas do curso pedagogia-cidadã

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Trajetórias afetivo-sexuais entre jovens do ensino médio: implicações dos sentidos de amor e maternidade

Pós-graduação em Educação - FCT

Cultura, linguagem e ensino da língua estrangeira: um estudo acerca desta inter-relação

Pós-graduação em Estudos Linguísticos - IBILCE