The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Support Vector Machine Regression for Volatile Stock Market Prediction
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
A type-2 fuzzy rule-based expert system model for stock price analysis
Expert Systems with Applications: An International Journal
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Genetically optimized fuzzy polynomial neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
Efficient hyperkernel learning using second-order cone programming
IEEE Transactions on Neural Networks
A Hybrid Neurogenetic Approach for Stock Forecasting
IEEE Transactions on Neural Networks
Forecasting stock indices with wavelet domain kernel partial least square regressions
Applied Soft Computing
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Multi-Kernel based feature selection for regression
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
An efficient multiple-kernel learning for pattern classification
Expert Systems with Applications: An International Journal
lp-norm multikernel learning approach for stock market price forecasting
Computational Intelligence and Neuroscience
| Hi-index | 12.05 |
Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple-kernel learning was developed to deal with this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously derived through semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method. By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Besides, the user need not specify the hyperparameter settings in advance, and trial-and-error for determining appropriate hyperparameter settings can then be avoided. Experimental results, obtained by running on datasets taken from Taiwan Capitalization Weighted Stock Index, show that our method performs better than other methods.