Making large-scale support vector machine learning practical
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Fast and accurate text classification via multiple linear discriminant projections
The VLDB Journal — The International Journal on Very Large Data Bases
Selecting the best hyperplane in the framework of optimal pairwise linear classifiers
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Breast cancer diagnosis using genetic programming generated feature
Pattern Recognition
Fast and accurate text classification via multiple linear discriminant projections
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Haar-like features with optimally weighted rectangles for rapid object detection
Pattern Recognition
EEG data classification through signal spatial redistribution and optimized linear discriminants
Computer Methods and Programs in Biomedicine
Support vector-based feature selection using Fisher's linear discriminant and Support Vector Machine
Expert Systems with Applications: An International Journal
A theoretical comparison of two linear dimensionality reduction techniques
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
A new approach to multi-class linear dimensionality reduction
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
On optimizing kernel-based fisher discriminant analysis using prototype reduction schemes
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A new linear dimensionality reduction technique based on chernoff distance
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
On the performance of chernoff-distance-based linear dimensionality reduction techniques
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Kernel sparse locality preserving canonical correlation analysis for multi-modal feature extraction
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
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Discriminants are often used in pattern recognition to separate clusters of points in some multidimensional 驴feature驴 space. This paper provides two fast and simple techniques for improving on the classification performance provided by Fisher's linear discriminant for two classes. Both of these methods are also extended to nonlinear decision surfaces through the use of Mercer kernels.