Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Shrinking the tube: a new support vector regression algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Recognition of Unconstrained Handwritten Numerals by Doubly Self-Organizing Neural Network
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
One-class texture classifier in the CCR feature space
Pattern Recognition Letters
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
Text classification from positive and unlabeled documents
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Single-Class Classification with Mapping Convergence
Machine Learning
Automatic new topic identification using multiple linear regression
Information Processing and Management: an International Journal
Blocking objectionable web content by leveraging multiple information sources
ACM SIGKDD Explorations Newsletter
Selection and Fusion of Color Models for Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From outliers to prototypes: Ordering data
Neurocomputing
Learning Bayesian classifiers from positive and unlabeled examples
Pattern Recognition Letters
Tunnel Hunter: Detecting application-layer tunnels with statistical fingerprinting
Computer Networks: The International Journal of Computer and Telecommunications Networking
An evaluation of dimension reduction techniques for one-class classification
Artificial Intelligence Review
Minimum spanning tree based one-class classifier
Neurocomputing
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
SVMC: single-class classification with support vector machines
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Visual categorization with negative examples for free
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Multiclass classification based on extended support vector data description
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental one-class learning with bounded computational complexity
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
ICNC'09 Proceedings of the 5th international conference on Natural computation
An analysis of generalization error in relevant subtask learning
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Learning Photometric Invariance for Object Detection
International Journal of Computer Vision
Masking of time-frequency patterns in applications of passive underwater target detection
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
DC models for spherical separation
Journal of Global Optimization
A survey of recent trends in one class classification
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Support vector neural training
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Outlier detection using ball descriptions with adjustable metric
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Swarm intelligent tuning of one-class v-SVM parameters
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Statistical processes monitoring based on improved ICA and SVDD
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Intrusion detection system based on multi-class SVM
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Redundant bit vectors for quickly searching high-dimensional regions
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Margin maximization in spherical separation
Computational Optimization and Applications
Pattern Recognition
Approximate polytope ensemble for one-class classification
Pattern Recognition
1-norm support vector novelty detection and its sparseness
Neural Networks
Density weighted support vector data description
Expert Systems with Applications: An International Journal
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In one-class classification, one class of data, called the target class, has to be distinguished from the rest of the feature space. It is assumed that only examples of the target class are available. This classifier has to be constructed such that objects not originating from the target set, by definition outlier objects, are not classified as target objects. In previous research the support vector data description (SVDD) is proposed to solve the problem of one-class classification. It models a hypersphere around the target set, and by the introduction of kernel functions, more flexible descriptions are obtained. In the original optimization of the SVDD, two parameters have to be given beforehand by the user. To automatically optimize the values for these parameters, the error on both the target and outlier data has to be estimated. Because no outlier examples are available, we propose a method for generating artificial outliers, uniformly distributed in a hypersphere. An (relative) efficient estimate for the volume covered by the one-class classifiers is obtained, and so an estimate for the outlier error. Results are shown for artificial data and for real world data.