Statistical Process Monitoring of Artificial Neural Networks
From MaRDI portal
Publication:6631184
DOI10.1080/00401706.2023.2239886MaRDI QIDQ6631184
Pavlo Mozharovskyi, Unnamed Author, Anna Malinovskaya
Publication date: 31 October 2024
Published in: Technometrics (Search for Journal in Brave)
Cites Work
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- Fast nonparametric classification based on data depth
- Sliced Regression for Dimension Reduction
- Sparse Sliced Inverse Regression Via Lasso
- Approximate computation of projection depths
- On a notion of data depth based on random simplices
- Novelty detection: a review. I. Statistical approaches
- Novelty detection: a review. II. Neural network based approaches
- Nonparametric depth-based multivariate outlier identifiers, and masking robustness properties
- Breakdown properties of location estimates based on halfspace depth and projected outlyingness
- Halfspace depth and regression depth characterize the empirical distribution
- General notions of statistical depth function.
- Host-based intrusion detection using dynamic and static behavioral models
- Choosing among notions of multivariate depth statistics
- Control charts based on parameter depths
- Nonparametric control charts based on data depth for location parameter
- Estimating the Support of a High-Dimensional Distribution
- Control Charts for Multivariate Processes
- A spatial rank-based multivariate EWMA control chart
- Sliced Inverse Regression for Dimension Reduction
- Variable Kernel Estimates of Multivariate Densities
- A Quality Index Based on Data Depth and Multivariate Rank Tests
- DD-Classifier: Nonparametric Classification Procedure Based onDD-Plot
- A survey on concept drift adaptation
- Advances in Artificial Intelligence – SBIA 2004
- Robustness and Complex Data Structures
- Sparse sufficient dimension reduction
- On Estimation of a Probability Density Function and Mode
- Sufficient Dimension Reduction via Inverse Regression
- Multivariate process control charts based on the \(L^p\) depth
- Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors
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