An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Journal of Intelligent Information Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The Perceptron Algorithm with Uneven Margins
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Kernel independent component analysis
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Advanced learning algorithms for cross-language patent retrieval and classification
Information Processing and Management: an International Journal
Proceedings of the 24th international conference on Machine learning
Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
A New Canonical Correlation Analysis Algorithm with Local Discrimination
Neural Processing Letters
A refinement framework for cross language text categorization
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Transfer learning via multi-view principal component analysis
Journal of Computer Science and Technology - Special issue on natural language processing
Matching samples of multiple views
Data Mining and Knowledge Discovery
Semi-supervised kernel canonical correlation analysis with application to human fMRI
Pattern Recognition Letters
Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search
International Journal of Computer Vision
Hinge loss bound approach for surrogate supervision multi-view learning
Pattern Recognition Letters
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Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese---English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages.