Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Sequential Hybrid Forecasting System for Demand Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
An Incremental Learning Algorithm Based on Support Vector Domain Classifier
ICCI '06 Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02
Hybrid Repayment Prediction for Debt Portfolio
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Prediction of Sequential Values for Debt Recovery
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Individual sequence prediction using memory-efficient context trees
IEEE Transactions on Information Theory
Multidimensional Social Network in the Social Recommender System
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Ensemble methods of incremental prediction for sequences refer to learning from new reference data that become available after the model has already been created from a previously available data set. The main obstacle in the prediction of sequential values in the real environment with huge amount of data is the integration of knowledge stored in the previously obtained models and the new knowledge derived from the incrementally acquired new increases of the data. In the paper, the new approach of the ensemble incremental learning for prediction of sequences was proposed as well as examined using real debt recovery data.