C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Machine Learning
Machine Learning
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Semi-Supervised Self-Training of Object Detection Models
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Machine Learning
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Bio-mimetic learning from images using imprecise expert information
Fuzzy Sets and Systems
Keypoint Signatures for Fast Learning and Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Semi-Supervised Learning
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Collaborative track analysis, data cleansing, and labeling
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Deep feature learning using target priors with applications in ECoG signal decoding for BCI
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The development of robust classification model is among the important issues in computer vision. This paper deals with weakly supervised learning that generalizes the supervised and semi-supervised learning. In weakly supervised learning training data are given as the priors of each class for each sample. We first propose a weakly supervised strategy for learning soft decision trees. Besides, the introduction of class priors for training samples instead of hard class labels makes natural the formulation of an iterative learning procedure. We report experiments for UCI object recognition datasets. These experiments show that recognition performance close to the supervised learning can be expected using the propose framework. Besides, an application to semi-supervised learning, which can be regarded as a particular case of weakly supervised learning, further demonstrates the pertinence of the contribution. We further discuss the relevance of weakly supervised learning for computer vision applications.