Name
Title | ||
|---|---|---|
Inductive conformal predictor for convolutional neural networks: Applications to active learning for image classification. |
Abstract | ||
|---|---|---|
•Inductive conformal predictors using nonconformity measures designed for convolutional neural networks produce reliable confidence values•The combination of informativeness, diversity, and information density in a single query function improves the performance of active learning•Distance metric learning produces similarity measures that adapt to the databases being used, improving the performance of query functions for active learning•Dimensionality reduction through principal component analysis significantly reduces the computational load of distance metric learning |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2019.01.035 | Pattern Recognition |
Keywords | Field | DocType |
Conformal prediction,Convolutional neural networks,Active learning,Distance metric learning,Image classification | Active learning,Pattern recognition,Similarity measure,Convolutional neural network,Outlier,Metric (mathematics),Conformal map,Artificial intelligence,Contextual image classification,Mathematics,Machine learning,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
90 | 1 | 0031-3203 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergio Matiz | 1 | 1 | 1.02 |
| Kenneth E. Barner | 2 | 812 | 70.19 |