Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data
From MaRDI portal
Publication:1009320
DOI10.1007/S10994-008-5092-4zbMath1200.68190OpenAlexW2117345453MaRDI QIDQ1009320
Randa Kassab, Frédéric Alexandre
Publication date: 31 March 2009
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-008-5092-4
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Novelty detection: a review. I. Statistical approaches
- Novelty detection: a review. II. Neural network based approaches
- Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements
- Generalized inverses. Theory and applications.
- Estimating the Support of a High-Dimensional Distribution
- 10.1162/15324430260185574
- 10.1162/15324430260185583
- Control Procedures for Residuals Associated with Principal Component Analysis
- Some Applications of the Pseudoinverse of a Matrix
- An algorithm for information structuring and retrieval
- Supervised versus unsupervised binary-learning by feedforward neural networks
This page was built for publication: Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data