Estimating the Support of a High-Dimensional Distribution
Bernhard Schölkopf, John Platt, John Shawe‐Taylor, Alex Smola, Robert C. Williamson
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Citation edge evidence
- Anomaly detection→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Learning from Imbalanced Data→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Support Vector Data Description→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Deep Learning for Anomaly Detection→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- A kernel two-sample test→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Editorial→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Support Vector Method for Novelty Detection→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Isolation-Based Anomaly Detection→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- A Kernel Method for the Two-Sample-Problem→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Classification in the Presence of Label Noise: A Survey→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Deep Learning for Anomaly Detection: A Review→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Kernel methods in machine learning→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Correcting Sample Selection Bias by Unlabeled Data→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Toward Open Set Recognition→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→Statistical Learning TheoryLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→Advances in kernel methods: support vector learningLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→Making Large-Scale SVM Learning PracticalLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→An introduction to kernel-based learning algorithmsLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→Improvements to Platt's SMO Algorithm for SVM Classifier DesignLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→Making Large-Scale Support Vector Machine Learning PracticalLegacy edge — source detail unavailable
- Support Vector Data Description→Statistical Learning TheoryLegacy edge — source detail unavailable
- Support Vector Data Description→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Isolation-Based Anomaly Detection→Support Vector Data DescriptionLegacy edge — source detail unavailable
- Isolation-Based Anomaly Detection→Anomaly detectionLegacy edge — source detail unavailable
- Support Vector Method for Novelty Detection→Statistical Learning TheoryLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Making Large-Scale Support Vector Machine Learning PracticalLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Making Large-Scale SVM Learning PracticalLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Advances in kernel methods: support vector learningLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Support Vector Method for Novelty DetectionLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Statistical Learning TheoryLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Improvements to Platt's SMO Algorithm for SVM Classifier DesignLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Correcting Sample Selection Bias by Unlabeled Data→A Kernel Method for the Two-Sample-ProblemLegacy edge — source detail unavailable
- Learning from Imbalanced Data→EditorialLegacy edge — source detail unavailable
- Learning from Imbalanced Data→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Anomaly detection→Support Vector Data DescriptionLegacy edge — source detail unavailable
- Anomaly detection→EditorialLegacy edge — source detail unavailable
- Anomaly detection→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Statistical Learning Theory→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Improvements to Platt's SMO Algorithm for SVM Classifier Design→Making Large-Scale Support Vector Machine Learning PracticalLegacy edge — source detail unavailable
- Improvements to Platt's SMO Algorithm for SVM Classifier Design→An introduction to kernel-based learning algorithmsLegacy edge — source detail unavailable
- Improvements to Platt's SMO Algorithm for SVM Classifier Design→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Making Large-Scale SVM Learning PracticalLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Advances in kernel methods: support vector learningLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Statistical Learning TheoryLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Classification in the Presence of Label Noise: A Survey→Support Vector Method for Novelty DetectionLegacy edge — source detail unavailable
- Classification in the Presence of Label Noise: A Survey→Anomaly detectionLegacy edge — source detail unavailable
- Kernel methods in machine learning→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- Kernel methods in machine learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- A kernel two-sample test→A Kernel Method for the Two-Sample-ProblemLegacy edge — source detail unavailable
- Deep Learning for Anomaly Detection→Support Vector Data DescriptionLegacy edge — source detail unavailable
- Deep Learning for Anomaly Detection→Isolation-Based Anomaly DetectionLegacy edge — source detail unavailable
- Deep Learning for Anomaly Detection→Anomaly detectionLegacy edge — source detail unavailable
- Deep Learning for Anomaly Detection: A Review→Support Vector Data DescriptionLegacy edge — source detail unavailable
- Deep Learning for Anomaly Detection: A Review→Isolation-Based Anomaly DetectionLegacy edge — source detail unavailable
- Deep Learning for Anomaly Detection: A Review→Anomaly detectionLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Anomaly detection | 2009 | 11,511 |
| Learning from Imbalanced Data | 2009 | 10,209 |
| Support Vector Data Description | 2003 | 3,526 |
| An introduction to kernel-based learning algorithms | 2001 | 3,498 |
| Deep Learning for Anomaly Detection | 2021 | 2,635 |
| A kernel two-sample test | 2012 | 2,224 |
| Editorial | 2004 | 2,081 |
| Support Vector Method for Novelty Detection | 1999 | 2,067 |
| Isolation-Based Anomaly Detection | 2012 | 2,055 |
| A Kernel Method for the Two-Sample-Problem | 2007 | 1,949 |
| kernlab - An S4 Package for Kernel Methods in R | 2004 | 1,864 |
| Classification in the Presence of Label Noise: A Survey | 2014 | 1,777 |
| Deep Learning for Anomaly Detection: A Review | 2020 | 1,731 |
| Kernel methods in machine learning | 2008 | 1,618 |
| Correcting Sample Selection Bias by Unlabeled Data | 2007 | 1,566 |
| Toward Open Set Recognition | 2012 | 1,527 |
Links
Topics
| Machine Learning and Data Classification | Computer Science |
| Face and Expression Recognition | Computer Science |
| Machine Learning and Algorithms | Computer Science |
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