Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Efficient kernel feature extraction for massive data sets
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A novel multi-view learning developed from single-view patterns
Pattern Recognition
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Existing multi-view learning focuses on the problem of how to learn from data represented by multiple independent sets of attributes (termed as multi-view data), and has been proved to bring an excellent performance. However, in general, we have only a single set of attributes (termed as single-view data) available. The goal of this paper is to employ the multi-view viewpoint to develop a multi-view kernel machine for such a single-view data. The key of doing so is to associate each learning machine with one kernel, take it as one view and thus form a set of learning machines from their corresponding kernels, as a result, a multi-view kernel machine can be developed by synthesizing them into a single learning framework. Further, in the two-view (two-kernel) case, we explore the relationship between the generalization ability of the proposed kernel machine and its associated kernels, in which with the kernel alignment (KA) as a correlation measure between kernels, it is found that superior performance of the proposed machine results from a weaker correlation between the constitutive kernels. To the best of our knowledge, both the multi-view learning on single-view data and the KA measure used here have not appeared in any literature. In practice, we take the kernel modified Ho-Kashyap with squared (KMHKS) approximation of the misclassification errors as a learning machine to develop a multi-view KMHKS (MultiV-KMHKS) on single-view data.