Information Sciences—Intelligent Systems: An International Journal
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
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
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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
Use of aggregation pheromone density for image segmentation
Pattern Recognition Letters
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Aggregation Pheromone Density Based Pattern Classification
Fundamenta Informaticae
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Ant based semi-supervised classification
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
A fast quasi-Newton method for semi-supervised SVM
Pattern Recognition
Efficient semi-supervised learning on locally informative multiple graphs
Pattern Recognition
Supervised neighborhood graph construction for semi-supervised classification
Pattern Recognition
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
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This article presents a novel 'self-training' based semi-supervised classification algorithm using the property of aggregation pheromone found in real ants. The proposed method has no assumption regarding the data distribution and is free from parameters to be set by the user. It can also capture arbitrary shapes of the classes. The proposed algorithm is evaluated with a number of synthetic as well as real life benchmark datasets in terms of accuracy, macro and micro averaged F"1 measures. Results are compared with two supervised and three semi-supervised classification techniques and are statistically validated using paired t-test. Experimental results show the potentiality of the proposed algorithm.