engleski

Predicting the Critical Number of Layers for Hierarchical Support Vector Regression

Hierarchical support vector regression (HSVR) models a function from data as a linearcombination of SVR models at a range of scales, starting at a coarse scale and moving to finer scalesas the hierarchy continues. In the original formulation of HSVR, there were no rules for choosingthe depth of the model. In this paper, we observe in a number of models a phase transition in thetraining error—the error remains relatively constant as layers are added, until a critical scale is passed, at which point the training error drops close to zero and remains nearly constant for added layers.We introduce a method to predict this critical scale a priori with the prediction based on the supportof either a Fourier transform of the data or the Dynamic Mode Decomposition (DMD) spectrum. Thisallows us to determine the required number of layers prior to training any model

support vector regression ; Fourier transform ; dynamic mode decomposition ; Koopman operator

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