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
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Neural Networks: A Comprehensive Foundation
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Ant Colony Optimization
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Semi-Supervised Self-Training of Object Detection Models
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
The Journal of Machine Learning Research
Aggregation pheromone density based data clustering
Information Sciences: an International Journal
Aggregation Pheromone Density Based Pattern Classification
Fundamenta Informaticae
Aggregation pheromone metaphor for semi-supervised classification
Pattern Recognition
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Semi-supervised classification methods make use of the large amounts of relatively inexpensive available unlabeled data along with the small amount of labeled data to improve the accuracy of the classification. This article presents a novel 'self-training' based semi-supervised classification algorithm using the property of aggregation pheromone found in natural behavior of real ants. The proposed algorithm is evaluated with real life benchmark data sets in terms of classification accuracy. Also the method is compared with two conventional supervised classification methods and two recent semi-supervised classification techniques. Experimental results show the potentiality of the proposed algorithm.