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Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation
While deep neural networks have succeeded in several visual applications, such as object recognition, detection, and localization, by reaching very high classification accuracies, it is important to note that many real-world applications demand varying costs for different types of misclassification errors, thus requiring cost-sensitive classification algorithms.
Toward Implicit Sample Noise Modeling: Deviation-driven Matrix Factorization
Specifically, by modeling and learning the deviation of data, we design a novel matrix factorization model.
Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning
Deep learning has been one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications where automatic feature extraction is needed.
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