Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A robust minimax approach to classification
The Journal of Machine Learning Research
Learning large margin classifiers locally and globally
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Semi-supervised protein classification using cluster kernels
Bioinformatics
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Semi-Supervised Learning
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
Help-Training for semi-supervised support vector machines
Pattern Recognition
Relaxed exponential kernels for unsupervised learning
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Efficient semi-supervised learning on locally informative multiple graphs
Pattern Recognition
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
Supervised neighborhood graph construction for semi-supervised classification
Pattern Recognition
Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier
IEEE Transactions on Neural Networks
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Semi-supervised learning (SSL) involves the training of a decision rule from both labeled and unlabeled data. In this paper, we propose a novel SSL algorithm based on the multiple clusters per class assumption. The proposed algorithm consists of two stages. In the first stage, we aim to capture the local cluster structure of the training data by using the k-nearest-neighbor (kNN) algorithm to split the data into a number of disjoint subsets. In the second stage, a maximal margin classifier based on the second order cone programming (SOCP) is introduced to learn an inductive decision function from the obtained subsets globally. For linear classification problems, once the kNN algorithm has been performed, the proposed algorithm trains a classifier using only the first and second order moments of the subsets without considering individual data points. Since the number of subsets is usually much smaller than the number of training points, the proposed algorithm is efficient for handling big data sets with a large amount of unlabeled data. Despite its simplicity, the classification performance of the proposed algorithm is guaranteed by the maximal margin classifier. We demonstrate the efficiency and effectiveness of the proposed algorithm on both synthetic and real-world data sets.