Ricardo B. C. Prudêncio
36 publications
8 venues
H Index 11
Affiliation
Federal University of Pernambuco, Recife, BrazilLinks
| Name | Venue | Year | citations |
|---|---|---|---|
Active semi-supervised learning for multi-target regression.
|
IJCNN | 2025 | 0 |
| CLAIRE: clustering evaluation based on item response theory and model agreement.
|
MLJ | 2025 | 0 |
| Novel applications of item response theory for analysing data set complexity and benchmark selection.
|
MLJ | 2025 | 0 |
| Measuring Latent Traits of Instance Hardness and Classifier Ability using Boltzmann Machines.
|
IJCNN | 2024 | 2 |
| Low-Pass Filter Application for Anomaly Detection with Sparse Autoencoder.
|
IJCNN | 2024 | 0 |
| Assessor Models for Explaining Instance Hardness in Classification Problems.
|
IJCNN | 2024 | 4 |
| Meta-Learning and Novelty Detection for Machine Learning with Reject Option.
|
IJCNN | 2024 | 1 |
| A Clustering-Based Method to Anomaly Detection in Thermal Power Plants.
|
IJCNN | 2022 | 2 |
| One-Class Classification for Selecting Synthetic Datasets in Meta-Learning.
|
IJCNN | 2020 | 0 |
| Item Response Theory for Evaluating Regression Algorithms.
|
IJCNN | 2020 | 6 |
| Item Response Theory to Estimate the Latent Ability of Speech Synthesizers.
|
ECAI | 2020 | 4 |
| Item response theory in AI: Analysing machine learning classifiers at the instance level.
|
Artificial Intelligence | 2019 | 121 |
| Cost Sensitive Evaluation of Instance Hardness in Machine Learning.
|
ECML/PKDD | 2019 | 5 |
| $β^3$-IRT: A New Item Response Model and its Applications.
|
AISTATS | 2019 | 0 |
| Transferring Knowledge From Texts to Images by Combining Deep Semantic Feature Descriptors.
|
IJCNN | 2018 | 0 |
| Data complexity meta-features for regression problems.
|
MLJ | 2018 | 0 |
| Making Sense of Item Response Theory in Machine Learning.
|
ECAI | 2016 | 84 |
| I/S-Race: An iterative Multi-Objective Racing Algorithm for the SVM Parameter Selection Problem.
|
ESANN | 2015 | 4 |
| Versatile Decision Trees for Learning Over Multiple Contexts.
|
ECML/PKDD | 2015 | 11 |
| Fine-tuning of support vector machine parameters using racing algorithms.
|
ESANN | 2014 | 11 |
| A collaborative filtering framework based on local and global similarities with similarity tie-breaking criteria.
|
IJCNN | 2014 | 3 |
| Active selection of training instances for a random forest meta-learner.
|
IJCNN | 2013 | 1 |
| Active testing for SVM parameter selection.
|
IJCNN | 2013 | 10 |
| Time Series Based Link Prediction.
|
IJCNN | 2012 | 97 |
| Multi-objective optimization and Meta-learning for SVM parameter selection.
|
IJCNN | 2012 | 20 |
| Uncertainty Sampling-Based Active Selection of Datasetoids for Meta-learning.
|
ICANN | 2011 | 10 |
| Uncertainty sampling methods for selecting datasets in active meta-learning.
|
IJCNN | 2011 | 12 |
| Supervised link prediction in weighted networks.
|
IJCNN | 2011 | 129 |
| Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data.
|
ICANN | 2009 | 32 |
| Active Generation of Training Examples in Meta-Regression.
|
ICANN | 2009 | 5 |
| Active Meta-Learning with Uncertainty Sampling and Outlier Detection.
|
IJCNN | 2008 | 10 |
| Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors.
|
ICANN | 2008 | 32 |
| Ranking and selecting clustering algorithms using a meta-learning approach.
|
IJCNN | 2008 | 91 |
| Active Learning to Support the Generation of Meta-examples.
|
ICANN | 2007 | 7 |
| A Machine Learning Approach to Define Weights for Linear Combination of Forecasts.
|
ICANN | 2006 | 7 |
| Selecting and Ranking Time Series Models Using the NOEMON Approach.
|
ICANN | 2003 | 5 |