Classifier systems and genetic algorithms
Artificial Intelligence
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Applications of machine learning and rule induction
Communications of the ACM
Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data
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
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
Learning word normalization using word suffix and context from unlabeled data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Modified support vector novelty detector using training data with outliers
Pattern Recognition Letters
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
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
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Partially supervised clustering for image segmentation
Pattern Recognition
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
IEEE Transactions on Neural Networks
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In this paper, a fuzzy semi-supervised support vector machines (FSSSVM) algorithm is proposed. It tries to overcome the need for a large labelled training set to learn accurate classifiers. For this, it uses both labelled and unlabelled data for training. It also modulates the effect of the unlabelled data in the learning process. Empirical evaluations showed that by additionally using unlabelled data, FSS-SVM requires less labelled training data than its supervised version, support vector machines, to achieve the same level of classification performance. Also, the incorporated fuzzy membership values of the unlabelled training patterns in the learning process have positively influenced the classification performance in comparison with its crisp variant.