PROJECT TITLE :

Hierarchical Approach for Multiscale Support Vector Regression

ABSTRACT:

Support vector regression (SVR) is predicated on a linear combination of displaced replicas of the same perform, referred to as a kernel. When the function to be approximated is nonstationary, the only kernel approach might be ineffective, as it's not able to follow the variations in the frequency content in the various regions of the input space. The hierarchical support vector regression (HSVR) model presented here aims to supply a sensible resolution also in these cases. HSVR consists of a set of hierarchical layers, each containing a customary SVR with Gaussian kernel at a given scale. Decreasing the size layer by layer, details are incorporated inside the regression operate. HSVR has been widely applied to noisy artificial and real datasets and it's shown the power in denoising the first information, getting an effective multiscale reconstruction of higher quality than that obtained by standard SVR. Results conjointly compare favorably with multikernel approaches. Furthermore, tuning the SVR configuration parameters is strongly simplified within the HSVR model.


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