Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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
Computers and Operations Research - Special issue: Emerging economics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Computers and Operations Research
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
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
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Design and implementation of NN5 for Hong Kong stock price forecasting
Engineering Applications of Artificial Intelligence
Application of wrapper approach and composite classifier to the stock trend prediction
Expert Systems with Applications: An International Journal
Graph sharpening plus graph integration
Bioinformatics
Soft-supervised learning for text classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Artificial neural networks with evolutionary instance selection for financial forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Stock price prediction is a field that has been continuously interesting. Stock prices are influenced by many factors such as oil prices, exchange rates, money interest rates, stock price indexes in other countries, and economic situations. Although these factors affect the stock price independently, they have an influence on the stock price through a complex interrelation, i.e., a network structure between these factors. In the stock prediction, the conventional methods represent limitations in reflecting the interrelation and complexity in these factors. In this paper, a stock prediction method using a semi-supervised learning (SSL) algorithm is proposed to circumvent such limitations. The SSL algorithm is a method that can implement a network consisting of nodes of the factors and edges of similarities between them. Through the network structure, the SSL algorithm is able to reflect the reciprocal and cyclic influences among the factors to prediction. The proposed model is applied to the stock price prediction from January 2007 to August 2008, using the global economic index and the stock prices of 200 individual companies listed to the KOSPI200.