Machine Learning
SIAM Review
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Lagrangian support vector machines
The Journal of Machine Learning Research
A robust minimax approach to classification
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Neural Computation
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
ICML '06 Proceedings of the 23rd international conference on Machine learning
Domain described support vector classifier for multi-classification problems
Pattern Recognition
A robust algorithm for quadratic optimization under quadratic constraints
Journal of Global Optimization
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Nearest hyperdisk methods for high-dimensional classification
Proceedings of the 25th international conference on Machine learning
Journal of Computational and Applied Mathematics
Multiclass classification based on extended support vector data description
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New clustering algorithms for the support vector machine based hierarchical classification
Pattern Recognition Letters
Large margin classifiers based on affine hulls
Neurocomputing
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Pattern classification via single spheres
DS'05 Proceedings of the 8th international conference on Discovery Science
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Large Margin Classifier Based on Hyperdisks
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 01
| Hi-index | 0.01 |
We introduce a large margin linear binary classification framework that approximates each class with a hyperdisk - the intersection of the affine support and the bounding hypersphere of its training samples in feature space - and then finds the linear classifier that maximizes the margin separating the two hyperdisks. We contrast this with Support Vector Machines (SVMs), which find the maximum-margin separator of the pointwise convex hulls of the training samples, arguing that replacing convex hulls with looser convex class models such as hyperdisks provides safer margin estimates that improve the accuracy on some problems. Both the hyperdisks and their separators are found by solving simple quadratic programs. The method is extended to nonlinear feature spaces using the kernel trick, and multi-class problems are dealt with by combining binary classifiers in the same ways as for SVMs. Experiments on a range of data sets show that the method compares favourably with other popular large margin classifiers.