[HTML][HTML] Managing the unknown in machine learning: Definitions, related areas, recent advances, and prospects
In the rapidly evolving domain of machine learning, the ability to adapt to unforeseen
circumstances and novel data types is of paramount importance. The deployment of Artificial …
circumstances and novel data types is of paramount importance. The deployment of Artificial …
Multi-modal data novelty detection with adversarial autoencoders
Z Chen, K Zhao, R Sun - Applied Soft Computing, 2024 - Elsevier
Novelty detection is usually defined as the identification of new or abnormal objects
(outliers) from the normal ones (inliers), which has wide potential applications including …
(outliers) from the normal ones (inliers), which has wide potential applications including …
Enhancing mass spectrometry data analysis: A novel framework for calibration, outlier detection, and classification
W Peng, T Zhou, Y Chen - Pattern Recognition Letters, 2024 - Elsevier
Mass spectrometry (MS) is a powerful analytical technique in metabolomics, enabling the
identification and quantification of metabolites. However, analyzing MS data poses …
identification and quantification of metabolites. However, analyzing MS data poses …
Managing the unknown: a survey on Open Set Recognition and tangential areas
In real-world scenarios classification models are often required to perform robustly when
predicting samples belonging to classes that have not appeared during its training stage …
predicting samples belonging to classes that have not appeared during its training stage …
On Incorporating new Variables during Evaluation
Any classification or regression model needs access to the same features and input that
were utilized to train the model. However in real world scenarios, several models are in …
were utilized to train the model. However in real world scenarios, several models are in …