Journal ArticleDOI
Support vector domain description
TLDR
This paper shows the use of a data domain description method, inspired by the support vector machine by Vapnik, called the support vectors domain description (SVDD), which can be used for novelty or outlier detection and is compared with other outlier Detection methods on real data.About:
This article is published in Pattern Recognition Letters.The article was published on 1999-11-01. It has received 1581 citations till now. The article focuses on the topics: Data domain & One-class classification.read more
Citations
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Journal ArticleDOI
Anomaly detection: A survey
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Book
Kernel Methods for Pattern Analysis
TL;DR: This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Journal ArticleDOI
Support vector machines
TL;DR: This issue's collection of essays should help familiarize readers with this interesting new racehorse in the Machine Learning stable, and give a practical guide and a new technique for implementing the algorithm efficiently.
Journal ArticleDOI
Support Vector Data Description
TL;DR: The Support Vector Data Description (SVDD) is presented which obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions.
Proceedings Article
Support Vector Method for Novelty Detection
TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data and is regularized by controlling the length of the weight vector in an associated feature space.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Proceedings ArticleDOI
Novelty detection for the identification of masses in mammograms
TL;DR: An alternative approach is explored in which a description of normality is attempted using the large number of available mammograms which do not show any evidence of mass-like structures to try and identify candidate masses in previously unseen images analysis and interpretation.
Novelty detection for the identification of masses in mammograms
TL;DR: In this paper, a neural network classifier is trained using the standard approach of minimising the mean-squared error (MSE) at the output, the underrepresented class will be ignored.
Proceedings Article
Data domain description using support vectors.
TL;DR: This paper introduces a new method for data domain description, inspired by the Support Vector Machine by V.Vapnik, which computes a sphere shaped decision boundary with minimal volume around a set of objects and contains support vectors describing the sphere boundary.