Learning with progressive transductive support vector machine

Authors:
Yisong Chen;Guoping Wang;Shihai Dong
Affiliations:
Department of Computer Science, HCI & Multimedia Lab, Peking University, Beijing 100871, China;Department of Computer Science, HCI & Multimedia Lab, Peking University, Beijing 100871, China;Department of Computer Science, HCI & Multimedia Lab, Peking University, Beijing 100871, China
Venue:
Pattern Recognition Letters
Year:
2003

Citing 10
Cited 16

A training algorithm for optimal margin classifiers

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to classify text from labeled and unlabeled documents

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Making large-scale support vector machine learning practical

Advances in kernel methods
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
Query Learning with Large Margin Classifiers

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning by transduction

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach

Pattern Recognition Letters
Optimization Techniques for Semi-Supervised Support Vector Machines

The Journal of Machine Learning Research
Transductive Learning from Relational Data

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A relational approach to probabilistic classification in a transductive setting

Engineering Applications of Artificial Intelligence
Learning with Sequential Minimal Transductive Support Vector Machine

FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
Semi-supervised learning for semantic parsing using support vector machines

NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
A bi-fuzzy progressive transductive support vector machine(BFPTSVM) algorithm

Expert Systems with Applications: An International Journal
Generating synopses for document-element search

Proceedings of the 18th ACM conference on Information and knowledge management
Distributed visual-target-surveillance system in wireless sensor networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new learning strategy for classification problems with different training and test distributions

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Time series forecasting for economic growth based on particle swarm optimization and support vector machine

IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Transductive learning from textual data with relevant example selection

DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Personalized mode transductive spanning SVM classification tree

Information Sciences: an International Journal
Semi-supervised SVMs for classification with unknown class proportions and a small labeled dataset

Proceedings of the 20th ACM international conference on Information and knowledge management
Summarizing figures, tables, and algorithms in scientific publications to augment search results

ACM Transactions on Information Systems (TOIS)
A novel T2-SVM for partially supervised classification

PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence

Quantified Score

Hi-index	0.10

Visualization

Abstract

Support vector machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead of an inductive one in support vector classifiers, the working set can be used as an additional source of information about margins. Compared with traditional inductive support vector machines, transductive support vector machine is often more powerful and can give better performance. In transduction, one estimates the classification function at points within the working set using information from both the training and the working set data. This will help to improve the generalization performance of SVMs, especially when training data is inadequate. Intuitively, we would expect transductive learning to yield improvements when the training sets are small or when there is a significant deviation between the training and working set subsamples of the total population. In this paper, a progressive transductive support vector machine is addressed to extend Joachims' transductive SVM to handle different class distributions. It solves the problem of having to estimate the ratio of positive/negative examples from the working set. The experimental results show the algorithm is very promising.