5 citations · 8 across the 4 of their papers we have counts for
5 papers
Explanation-by-Example Based on Item Response Theory
Lucas F. F. Cardoso, José de S. Ribeiro, Vitor C. A. Santos +4
Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have cha…
Meta-aprendizado para otimizacao de parametros de redes neurais
Tarsicio Lucas, Teresa Ludermir, Ricardo Prudencio +1
The optimization of Artificial Neural Networks (ANNs) is an important task to the success of using these models in real-world applications. The solutions adopted to this task are e…
Data vs classifiers, who wins?
Lucas F. F. Cardoso, Vitor C. A. Santos, Regiane S. Kawasaki Francês +2
The experiments covered by Machine Learning (ML) must consider two important aspects to assess the performance of a model: datasets and algorithms. Robust benchmarks are needed to…
Decoding machine learning benchmarks
Lucas F. F. Cardoso, Vitor C. A. Santos, Regiane S. K. Francês +2
Despite the availability of benchmark machine learning (ML) repositories (e.g., UCI, OpenML), there is no standard evaluation strategy yet capable of pointing out which is the best…
-IRT: A New Item Response Model and its Applications
Yu Chen, Telmo Silva Filho, Ricardo B. C. Prudêncio +2
Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels. In this…