Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Advanced learning algorithms for cross-language patent retrieval and classification
Information Processing and Management: an International Journal
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Human action classification using SVM_2K classifier on motion features
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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In this paper, we introduce a new method (SVM_2K) which amalgamates the capabilities of the Support Vector Machine (SVM) and Kernel Canonical Correlation Analysis (KCCA) to give a more sophisticated combination rule that the boosting framework allows. We show how this combination can be achieved within a unified optimisation model to create a consistent learning rule which combines the classification abilities of the individual SVMs with the synthesis abilities of KCCA. To solve the unified problem, we present an algorithm based on the Augmented Lagrangian Method. Experiments show that SVM_2K performs well on generic object recognition problems in computer vision.