Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Probabilistic Reasoning Models for Face Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Semantic classification in image databases
Semantic classification in image databases
Evolutionary feature synthesis for facial expression recognition
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Extracting gene regulation information for cancer classification
Pattern Recognition
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Identification of signatures in biomedical spectra using domain knowledge
Artificial Intelligence in Medicine
Image ratio features for facial expression recognition application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Classifying motor imagery EEG signals by iterative channel elimination according to compound weight
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Integrated classifier hyperplane placement and feature selection
Expert Systems with Applications: An International Journal
Framework for reliable, real-time facial expression recognition for low resolution images
Pattern Recognition Letters
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A linear programming technique is introduced that jointly performs feature selection and classifier training so that a subset of features is optimally selected together with the classifier. Because traditional classification methods in computer vision have used a two-step approach: feature selection followed by classifier training, feature selection has often been ad hoc, using heuristics or requiring a timeconsuming forward and backward search process. Moreover, it is difficult to determine which features to use and how many features to use when these two steps are separated. The linear programming technique used in this paper, which we call feature selection via linear programming (FSLP), can determine the number of features and which features to use in the resulting classification function based on recent results in optimization. We analyze why FSLP can avoid the curse of dimensionality problem based on margin analysis. As one demonstration of the performance of this FSLP technique for computer vision tasks, we apply it to the problem of face expression recognition. Recognition accuracy is compared with results using Support Vector Machines, the AdaBoost algorithm, and a Bayes classifier.