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PAMI
2011
14 years 9 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
GCB
2006
Springer
140views Biometrics» more  GCB 2006»
15 years 5 months ago
PALMA: Perfect Alignments using Large Margin Algorithms
: Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequen...
Gunnar Rätsch, Bettina Hepp, Uta Schulze, Che...
ICML
2009
IEEE
16 years 2 months ago
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning
Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unificatio...
Linli Xu, Martha White, Dale Schuurmans
CVPR
2006
IEEE
15 years 5 months ago
Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning
In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning...
Changbo Yang, Ming Dong, Jing Hua
CVPR
2008
IEEE
16 years 4 months ago
Semi-supervised SVM batch mode active learning for image retrieval
Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learni...
Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R....