Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Knowledge Engineering Review
Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Journal of the ACM (JACM)
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Gradient-Based Optimization of Hyperparameters
Neural Computation
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Graph-based Semi-supervised Learning Algorithm for Web Page Classification
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Fast protein classification with multiple networks
Bioinformatics
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Graph sharpening plus graph integration
Bioinformatics
Pattern Analysis & Applications
On Efficient Large Margin Semisupervised Learning: Method and Theory
The Journal of Machine Learning Research
Soft-supervised learning for text classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Graph-based semi-supervised learning as a generative model
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semi-supervised learning of visual classifiers from web images and text
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A graph-based semi-supervised learning for question-answering
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Semi-Supervised Learning
Expert Systems with Applications: An International Journal
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The generalization ability of a machine learning algorithm varies on the specified values to the model parameters and the degree of noise in the learning dataset. If the dataset has an enough amount of labeled data points, the optimal value for the model parameter can be found via validation by using a subset of the given dataset. However, for semi-supervised learning --one of the most recent learning algorithms, this is not as available as in conventional supervised learning. In semi-supervised learning, it is assumed that the dataset is given with only a few labeled data points. Therefore, holding out some of labeled data points for validation is not easy. The lack of labeled data points, furthermore, makes it difficult to estimate the degree of noise in the dataset. To circumvent the addressed difficulties, we propose to employ ensemble learning and graph sharpening. The former replaces the model parameter selection procedure to an ensemble network of the committee members trained with various values of model parameter. The latter, on the other hand, improves the performance of algorithms by removing unhelpful information caused by noise. The experimental results demonstrate the applicability of the proposed method for many real-world problems with no concern for the technical difficulties, by selecting the best parameter values and mitigating the influence of noise.