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Sobre

Sobre

Professor Adjunto no Departamento de Engenharia Informática da ESTG-Leiria (Instituto Politécnico de Leiria) e investigador no CRACS.

É doutorado em Ciência dos Computadores pela Universidade do Porto; mestre em Informática, ramo de sistemas e redes, também pela Universidade do Porto; licenciado em Engenharia Informática pelo Instituto Superior de Engenharia do Porto.

Coordena atualmente a Pós-Graduação em Informática de Segurança e Computação Forense a decorrer no IPLeiria e é responsável pela lecionação de unidades curriculares na área das redes de computadores, administração de sistemas e redes, tecnologias de cloud e infraestruturas de datacenters.

As principais áreas de investigação incluem os algoritmos imuno-inspirados para a deteção automática de anomalias, algoritmos de classificação e deteção usando ensembles, aprendizagem em sistemas dinâmicos e com base temporal.

Detém igualmente experiência empresarial como gestor de projetos TI e administrador de sistemas.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Mário João Antunes
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2009
Publicações

2018

Adaptive Learning Models Evaluation in Twitter’s Timelines

Autores
Costa, J; Silva, C; Antunes, M; Ribeiro, B;

Publicação
2018 International Joint Conference on Neural Networks (IJCNN)

Abstract

2017

Automatic Documents Counterfeit Classification Using Image Processing and Analysis

Autores
Vieira, R; Antunes, M; Silva, C; Assis, A;

Publicação
Pattern Recognition and Image Analysis - 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings

Abstract

2017

Performance Metrics for Model Fusion in Twitter Data Drifts

Autores
Costa, J; Silva, C; Antunes, M; Ribeiro, B;

Publicação
Pattern Recognition and Image Analysis - 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings

Abstract

2017

Adaptive learning for dynamic environments: A comparative approach

Autores
Costa, J; Silva, C; Antunes, M; Ribeiro, B;

Publicação
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Abstract
Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).

2016

Choice of Best Samples for Building Ensembles in Dynamic Environments

Autores
Costa, J; Silva, C; Antunes, M; Ribeiro, B;

Publicação
ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2016

Abstract
Machine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.

Teses
supervisionadas

2017

Using telemedicine WebRTC tests in hospital environment

Autor
Dário Gabriel da Cruz Santos

Instituição
IPLeiria