Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Incremental learning of bidirectional principal components for face recognition
Pattern Recognition
A machine learning approach to textual entailment recognition
Natural Language Engineering
From minimum enclosing ball to fast fuzzy inference system training on large datasets
IEEE Transactions on Fuzzy Systems
Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
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For handling data and training model, existing machine learning methods do not take timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods.