Robust regression methods for computer vision: a review
International Journal of Computer Vision
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Recognizing 3-D Objects with Linear Support Vector Machines
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
An Equivalence Between Sparse Approximation and Support Vector Machines
An Equivalence Between Sparse Approximation and Support Vector Machines
Antifaces: A Novel, Fast Method for Image Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and accurate text classification via multiple linear discriminant projections
The VLDB Journal — The International Journal on Very Large Data Bases
Using Discriminant Analysis for Multi-class Classification
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Fast and accurate text classification via multiple linear discriminant projections
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Identification of signatures in biomedical spectra using domain knowledge
Artificial Intelligence in Medicine
Support vector-based feature selection using Fisher's linear discriminant and Support Vector Machine
Expert Systems with Applications: An International Journal
Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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We show that the orientation and location of the separating hyperplane for 2-class supervised pattern classification obtained by the Support Vector Machine (SVM) proposed by Vapnik and his colleagues, is equivalent to the solution obtained by Fisher‘s Linear Discriminant on the set of Support Vectors. In other words, SVM can be seen as a way to ’sparsify‘ Fisher‘s Linear Discriminant in order to obtain the most generalizing classification from the training set.