Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Concept decompositions for large sparse text data using clustering
Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth 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
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Co-training with a Single Natural Feature Set Applied to Email Classification
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
A Semi-Supervised Document Clustering Algorithm Based on EM
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Personalized Spam Filtering with Semi-supervised Classifier Ensemble
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Customizable Instance-Driven Webpage Filtering Based on Semi-Supervised Learning
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Interactive Spam Filtering with Active Learning and Feature Selection
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Semi-Supervised Learning
A refinement approach to handling model misfit in semi-supervised learning
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Multi-layer graph-based semi-supervised learning for large-scale image datasets using mapreduce
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Phoneme Based Representation for Vietnamese Web Page Classification
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
A cluster-assumption based batch mode active learning technique
Pattern Recognition Letters
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In web mining, there are situations in which only few data is labeled which imposes difficulties on traditional web page classification algorithms. Active learning scheme is then proposed to sample the most representative unlabeled data, which are then annotated by external oracles. Most present active methods are based on series-mode query strategy, which deduces the process of active learning inefficient and unstable. In this paper, we propose a novel text oriented active semi-supervised classification model, which is so-called active SSC. Comparing with other active approaches, our model has the characteristic of comprehensibility, and thus it is easy to design a batch-mode query strategy. Experimental results on public text data showed our method is an effect and stable active approach.