Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Shrinking the tube: a new support vector regression algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Introduction to Linear Optimization
Introduction to Linear Optimization
ϵ-Descending Support Vector Machines for Financial Time Series Forecasting
Neural Processing Letters
IEEE Transactions on Knowledge and Data Engineering
Support Vector Machine Regression for Volatile Stock Market Prediction
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A robust minimax approach to classification
The Journal of Machine Learning Research
Convex Optimization
Learning large margin classifiers locally and globally
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
Regularized least squares fuzzy support vector regression for financial time series forecasting
Expert Systems with Applications: An International Journal
Learning classifiers from imbalanced data based on biased minimax probability machine
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Imbalanced learning with a biased minimax probability machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
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Time series prediction, especially financial time series prediction, is a challenging task in machine learning. In this issue, the data are usually non-stationary and volatile in nature. Because of its good generalization power, the support vector regression (SVR) has been widely applied in this application. The standard SVR employs a fixed @e-tube to tolerate noise and adopts the @?"p-norm (p=1 or 2) to model the functional complexity of the whole data set. One problem of the standard SVR is that it considers data in a global fashion only. Therefore it may lack the flexibility to capture the local trend of data; this is a critical aspect of volatile data, especially financial time series data. Aiming to attack this issue, we propose the localized support vector regression (LSVR) model. This novel model is demonstrated to provide a systematic and automatic scheme to adapt the margin locally and flexibly; while the margin in the standard SVR is fixed globally. Therefore, the LSVR can tolerate noise adaptively. The proposed LSVR is promising in the sense that it not only captures the local information in data, but more importantly, it establishes connection with several models. More specifically: (1) it can be regarded as the regression extension of a recently proposed promising classification model, the Maxi-Min Margin Machine; (2) it incorporates the standard SVR as a special case under certain mild assumptions. We provide both theoretical justifications and empirical evaluations for this novel model. The experimental results on synthetic data and real financial data demonstrate its advantages over the standard SVR.