C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Category learning through multimodality sensing
Neural Computation
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
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
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Machine Learning
Improving Short-Text Classification using Unlabeled Data for Classification Problems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Toward a Theory of Learning Coherent Concepts
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
Gene functional classification by semi-supervised learning from heterogeneous data
Proceedings of the 2003 ACM symposium on Applied computing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Multimodal integration-a statistical view
IEEE Transactions on Multimedia
IEEE Transactions on Information Theory - Part 2
Music artist style identification by semi-supervised learning from both lyrics and content
Proceedings of the 12th annual ACM international conference on Multimedia
Semi-supervised learning for music artists style identification
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Co-Adaptation of audio-visual speech and gesture classifiers
Proceedings of the 8th international conference on Multimodal interfaces
Knowledge and Information Systems
Music clustering with features from different information sources
IEEE Transactions on Multimedia - Special section on communities and media computing
Improving mood classification in music digital libraries by combining lyrics and audio
Proceedings of the 10th annual joint conference on Digital libraries
DCPE co-training for classification
Neurocomputing
A general approach for adaptive kernels in semi-supervised clustering
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Quality of information-based source assessment and selection
Neurocomputing
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This paper studies the use of a semisupervised learning algorithm from different information sources. We first offer a theoretical explanation as to why minimising the disagreement between individual models could lead to the performance improvement. Based on the observation, this paper proposes a semisupervised learning approach that attempts to minimise this disagreement by employing a co-updating method and making use of both labeled and unlabeled data. Three experiments to test the effectiveness of the approach are presented in this paper: (i) webpage classification from both content and hyperlinks; (ii) functional classification of gene using gene expression data and phylogenetic data and (iii) machine self-maintaining from both sensory and image data. The results show the effectiveness and efficiency of our approach and suggest its application potentials.