The nature of statistical learning theory
The nature of statistical learning theory
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '06 Proceedings of the 23rd international conference on Machine learning
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of smoothing technique on twin support vector machines
Pattern Recognition Letters
Nonparallel plane proximal classifier
Signal Processing
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Selecting informative universum sample for semi-supervised learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
{\cal U}Boost: Boosting with the Universum
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improvements on Twin Support Vector Machines
IEEE Transactions on Neural Networks
Practical Conditions for Effectiveness of the Universum Learning
IEEE Transactions on Neural Networks
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions
| Hi-index | 7.29 |
Universum samples, defined as samples not belonging to any class for a classification problem of interest, have been useful in supervised learning. Here we design a new nonparallel support vector machine (U-NSVM) that can exploit prior knowledge embedded in the universum to construct a more robust classifier for training. To this end, U-NSVM maximizes the two margins associated with the two closest neighboring classes, which is combined by two nonparallel hyperplanes. Therefore, U-NSVM has better flexibility and can yield a more reasonable classifier in most cases. In addition, our method includes fewer parameters than U-SVM, so is easier to implement. Experiments demonstrate that U-NSVM outperforms the traditional SVM and U-SVM.