Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On-line prediction with kernels and the complexity approximation principle
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Prediction, Learning, and Games
Prediction, Learning, and Games
Improving the aggregating algorithm for regression
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Online Regression Competitive with Changing Predictors
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
A dynamic threshold decision system for stock trading signal detection
Applied Soft Computing
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Consider the online regression problem where the dependence of the outcome yton the signal xtchanges with time. Standard regression techniques, like Ridge Regression, do not perform well in tasks of this type. We propose two methods to handle this problem: WeCKAAR, a simple modification of an existing regression technique, and KAARCh, an application of the Aggregating Algorithm. Empirical results on artificial data show that in this setting, KAARCh is superior to WeCKAAR and standard regression techniques. On options implied volatility data, the performance of both KAARCh and WeCKAAR is comparable to that of the proprietary technique currently being used at the Russian Trading System Stock Exchange (RTSSE).