Support Vector Machines for Nonlinear Kernel ARMA System Identification

Support Vector Machines for System Identification

Least Squares Support Vector Machines for Kernel CCA in Nonlinear State-Space Identification

We show that kernel canonical correlation analysis (KCCA) can be used to construct a state sequence of an unknown nonlinear dynamical system from delay vectors of inputs and outputs. In KCCA a feature map transforms the available data into a high dimensional feature space, where classical CCA is applied to find linear relations. The feature map is only implicitly defined through the choice of a...

Kernel/feature Selection for Support Vector Machines

Support Vector Machines are classifiers with architectures determined by kernel functions. In these proceedings we propose a method for selecting the best SVM kernel for a given classification problem. Our method searches for the best kernel by remapping the data via a kernel variant of the classical Gram-Schmidt orthonormalization procedure then using Fisher’s linear discriminant on the remapp...

Fast FPGA System for Training Nonlinear Support Vector Machines

Support Vector Machines (SVMs) are powerful supervised learning methods in machine learning. However, their applicability to large problems has been limited due to the time consuming training stage whose computational cost scales quadratically with the number of examples. In this work, a complete FPGAbased system for nonlinear SVM training using ensemble learning is presented. The proposed fram...

Support vector machines for nonlinear pavement backanalysis

Backanalysis or backcalculation of in-service pavement mechanical properties (such as elastic modulus) from pavement Non-Destructive Test (NDT) deflection data is a routine practice carried out by highway engineers for pavement structural condition evaluation, remaining life calculations, and mechanistic-based analysis. Owing to the complexity of this ill-conditioned inverse modeling problem, n...