Statistical analysis with missing data
Statistical analysis with missing data
Some Solutions to the Missing Feature Problem in Vision
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Learning with side information: PAC learning bounds
Journal of Computer and System Sciences
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of Face Resolution for Expression Analysis
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Machine Learning for Clinical Diagnosis from Functional Magnetic Resonance Imaging
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multi-Modal Tensor Face for Simultaneous Super-Resolution and Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Nightmare at test time: robust learning by feature deletion
ICML '06 Proceedings of the 23rd international conference on Machine learning
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning using hidden information (learning with teacher)
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Pattern classification with missing data: a review
Neural Computing and Applications - Special Issue - KES2008
Semi-Supervised Learning
Learning to classify with missing and corrupted features
Machine Learning
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
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In many problems of machine learning and computer vision, there exists side information, i.e., information contained in the training data and not available in the testing phase. This motivates the recent development of a new learning approach known as learning with side information that aims to incorporate side information for improved learning algorithms. In this work, we describe a new training method of boosting classifiers that uses side information, which we term as AdaBoost+. In particular, AdaBoost+ employs a novel classification label imputation method to construct extra weak classifiers from the available information that simulate the performance of better weak classifiers obtained from the features in side information. We apply our method to two problems, namely handwritten digit recognition and facial expression recognition from low resolution images, where it demonstrates its effectiveness in classification performance.