Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Graph-Based Semisupervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
Semantic Features for Multi-view Semi-supervised and Active Learning of Text Classification
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Semi-supervised learning with multiple views
Semi-supervised learning with multiple views
Semi-Supervised Learning
Sparse Semi-supervised Learning Using Conjugate Functions
The Journal of Machine Learning Research
View construction for multi-view semi-supervised learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Semi-supervised multitask learning via self-training and maximum entropy discrimination
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Multi-view maximum entropy discrimination
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose a new approach, multi-view Laplacian support vector machines (SVMs), for semi-supervised learning under the multi-view scenario. It integrates manifold regularization and multi-view regularization into the usual formulation of SVMs and is a natural extension of SVMs from supervised learning to multi-view semi-supervised learning. The function optimization problem in a reproducing kernel Hilbert space is converted to an optimization in a finite-dimensional Euclidean space. After providing a theoretical bound for the generalization performance of the proposed method, we further give a formulation of the empirical Rademacher complexity which affects the bound significantly. From this bound and the empirical Rademacher complexity, we can gain insights into the roles played by different regularization terms to the generalization performance. Experimental results on synthetic and real-world data sets are presented, which validate the effectiveness of the proposed multi-view Laplacian SVMs approach.