An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hybrid HMM-SVM Method for Online Handwriting Symbol Recognition
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
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Conditional density estimation is very important in financial engineer, risk management, and other engineering computing problem. However, most regression models have a latent assumption that the probability density is a Gaussian distribution, which is not necessarily true in many real life applications. In this paper, we give a framework to estimate or predict the conditional density mixture dynamically. Through combining the Input-Output HMM with SVM regression together and building a SVM model in each state of the HMM, we can estimate a conditional density mixture instead of a single gaussian. With each SVM in each node, this model can be applied for not only regression but classifications as well. We applied this model to denoise the ECG data. The proposed method has the potential to apply to other time series such as stock market return predictions.