The following pages link to Support vector data description (Q703096):
Displaying 50 items.
- Clustering-based ensembles for one-class classification (Q278701) (← links)
- Loda: lightweight on-line detector of anomalies (Q298359) (← links)
- Multi-level anomaly detection: relevance of big data analytics in networks (Q301768) (← links)
- The maximum vector-angular margin classifier and its fast training on large datasets using a core vector machine (Q456027) (← links)
- Theoretical analysis for solution of support vector data description (Q553272) (← links)
- Two-class support vector data description (Q614090) (← links)
- Anomaly detection combining one-class SVMs and particle swarm optimization algorithms (Q623887) (← links)
- A procedure using support vector data description and mutual information for end price assessment in online C2C auction (Q651359) (← links)
- The sparse signomial classification and regression model (Q744718) (← links)
- How to make a neural network say ``Don't know'' (Q781881) (← links)
- Fuzzy one-class support vector machines (Q835220) (← links)
- A fuzzy support vector classifier based on Bayesian optimization (Q928184) (← links)
- A boundary method for outlier detection based on support vector domain description (Q955827) (← links)
- One-class SVMs challenges in audio detection and classification applications (Q966918) (← links)
- An online core vector machine with adaptive MEB adjustment (Q991962) (← links)
- Classification in the presence of class noise using a probabilistic kernel Fisher method (Q996411) (← links)
- An online support vector machine for abnormal events detection (Q1031230) (← links)
- Noise peeling methods to improve boosting algorithms (Q1660240) (← links)
- One-class classification with extreme learning machine (Q1665625) (← links)
- Fault diagnosis in condition of sample type incompleteness using support vector data description (Q1665664) (← links)
- A new robust model of one-class classification by interval-valued training data using the triangular kernel (Q1669149) (← links)
- Smoothly approximated support vector domain description (Q1669703) (← links)
- Commentary: A decomposition of the outlier detection problem into a set of supervised learning problems (Q1689575) (← links)
- Expected similarity estimation for large-scale batch and streaming anomaly detection (Q1689600) (← links)
- Privacy preserving and fast decision for novelty detection using support vector data description (Q1708732) (← links)
- Explaining anomalies in groups with characterizing subspace rules (Q1741431) (← links)
- A precise ranking method for outlier detection (Q1750049) (← links)
- Cost-based feature selection for support vector machines: an application in credit scoring (Q1753611) (← links)
- A new maximum margin algorithm for one-class problems and its boosting implementation (Q1779737) (← links)
- A core set based large vector-angular region and margin approach for novelty detection (Q1792718) (← links)
- An improved semisupervised outlier detection algorithm based on adaptive feature weighted clustering (Q1793446) (← links)
- Multi-subspace factor analysis integrated with support vector data description for multimode process monitoring (Q1797206) (← links)
- One-class support vector ensembles for image segmentation and classification (Q1932899) (← links)
- Scaling up minimum enclosing ball with total soft margin for training on large datasets (Q1942728) (← links)
- A corporate credit rating model using support vector domain combined with fuzzy clustering algorithm (Q1954637) (← links)
- Data compression by volume prototypes for streaming data (Q1957882) (← links)
- Modeling of the safe region based on support vector data description for health assessment of wheelset bearings (Q1984923) (← links)
- Image anomalies: a review and synthesis of detection methods (Q1999478) (← links)
- An adaptive radial basis function kernel for support vector data description (Q2027243) (← links)
- Covering problems with polyellipsoids: a location analysis perspective (Q2028797) (← links)
- An overlap sensitive neural network for class imbalanced data (Q2036784) (← links)
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (Q2051254) (← links)
- Multi-class Bayesian support vector data description with anomalies (Q2095204) (← links)
- Optimised one-class classification performance (Q2102347) (← links)
- Computational adaptive multivariable degradation model for improving the remaining useful life prediction in industrial systems (Q2115017) (← links)
- Efficient SVDD sampling with approximation guarantees for the decision boundary (Q2163193) (← links)
- Interpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection (Q2163846) (← links)
- Visual object detection using cascades of binary and one-class classifiers (Q2193769) (← links)
- Large-scale distributed sparse class-imbalance learning (Q2198074) (← links)
- A review on distance based time series classification (Q2218332) (← links)
