Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Sparse Multiple Kernel Learning for Signal Processing Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
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In this paper, we address a regression problem for economic data forecasting by using multiple-kernel learning (MKL) and propose a novel two-step multiple-kernel regression (MKR) method. The proposed MKR method firstly reformulates learning from linear convex combination of the basis kernels as a maximum eigenvalue problem. The optimal weights of basis kernels in the combination can be conveniently derived from solving the maximum eigenvalue problem by eigenvalue decomposition instead of solving complicated optimization like most existing MKR algorithms. By means of SVR optimization routine, finally, we can learn from basis kernels which have different predictive ability so as to improve prediction performance. More significantly, the way to address MKR problem can make sense of the weights and the correspondingly optimal kernel in terms of interpretability. To evaluate performance, the proposed MKR method is compared with the state-of-the-art methods on three real sets of economics data. The experimental results prove that the proposed two-step MKR method outperforms the other methods in terms of prediction performance and model selection, and demonstrates satisfied efficiency.