A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
The use of bigrams to enhance text categorization
Information Processing and Management: an International Journal
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Optimization Approaches for Semi-Supervised Multiclass Classification
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Tags vs shelves: from social tagging to social classification
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
| Hi-index | 0.00 |
Support Vector Machines present an interesting and effective approach to solve automated classification tasks. Although it only handles binary and supervised problems by nature, it has been transformed into multiclass and semi-supervised approaches in several works. A previous study on supervised and semi-supervised SVM classification over binary taxonomies showed how the latter clearly outperforms the former, proving the suitability of unlabeled data for the learning phase in this kind of tasks. However, the suitability of unlabeled data for multiclass tasks using SVM has never been tested before. In this work, we present a study on whether unlabeled data could improve results for multiclass web page classification tasks using Support Vector Machines. As a conclusion, we encourage to rely only on labeled data, both for improving (or at least equaling) performance and for reducing the computational cost.