An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
Multi kernel learning with online-batch optimization
The Journal of Machine Learning Research
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
A Hybrid Neurogenetic Approach for Stock Forecasting
IEEE Transactions on Neural Networks
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Linear multiple kernel learning model has been used for predicting financial time series. However, l1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt lp-norm multiple kernel support vector regression (1 ≤ p l1-norm multiple support vector regression model.