Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Invariant Support Vector Machines
Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Invariances in kernel methods: From samples to objects
Pattern Recognition Letters
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
Structural Support Vector Machine
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
GLSVM: Integrating Structured Feature Selection and Large Margin Classification
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Boosting support vector machines for imbalanced data sets
Knowledge and Information Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
| Hi-index | 12.05 |
Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l"2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency.