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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
On-line ensemble SVM for robust object tracking
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Discriminative tracking by metric learning
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
NESVM: A Fast Gradient Method for Support Vector Machines
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Online multiple instance boosting for object detection
Neurocomputing
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improvements on Twin Support Vector Machines
IEEE Transactions on Neural Networks
Graph mode-based contextual kernels for robust SVM tracking
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Robust twin support vector machine for pattern classification
Pattern Recognition
Twin support vector machine with Universum data
Neural Networks
Machine Learning for Computer Vision
Machine Learning for Computer Vision
Structural twin support vector machine for classification
Knowledge-Based Systems
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In human's behavior and cognition, teachers always play an important role. However, in the field of machine learning, the information offered by the teacher is seldom applied. In this paper, inspired by Vapnik et al., we propose a fast learning model using privileged information, which uses two smaller-sized Linear Programming (LP) model to take place of a larger Quadratic Programming (QP) model and applies two nonparallel hyperplanes to construct the final classifier. After that, we introduce the Learning model Using Privileged Information (LUPI) into the Visual Tracking Object (VOT) field, which can accelerate the convergence rate of learning and effectively improve the quality. In detail, we give the clear definition of the privileged information about VOT problem and propose a simple but effective on-line object tracking algorithm using privileged information, and all experimental results show the robustness and effectiveness of the proposed method, at the same time show the privileged information provides a great help for further improving the quality.