A new regression technique based on Vapnik’s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend...

− Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields – time se...

The problem of tolerant data fitting by a nonlinear surface, induced by a kernel-based support vector machine [19], is formulated as a linear program with fewer number of variables than that of other linear programming formulations [17]. A generalization of the linear programming chunking algorithm [1] for arbitrary kernels [10] is implemented for solving problems with very large datasets where...

This paper deals with transforming Support vector regression (SVR) models into fuzzy systems (FIS). It is highlighted that trained support vector based models can be used for the construction of fuzzy rule-based regression models. However, the transformed support vector model does not automatically result in an interpretable fuzzy model. Training of a support vector model results a complex rule...

We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with an energy function akin to the unnormalized log-probability of the data. We show that the conditions for conservativeness are more general than for encoder and decoder weights to be the same (“tied weights”), and that they also depend on the form of the h...

Capacity utilization rate is one of the most important indicators of the efficiency of the manufacturing industry, and therefore of the return of the investments made. Estimation of these rates accurately renders it possible to make important economic decisions such as taking sectorial investment decisions, defining the optimal distribution of sectorial credits, determining noncompetitive secto...

Sustainable economic growth has become one of the goals concern for policymakers around world. Financial inclusion received significant attention in planning strong policies to achieve goals. This study aims analyze how fast response is due shocks financial depth, access, and stability 34 provinces Indonesia, 2014-2019. uses vector auto-regression (VAR) models Granger causality test main resear...

Penelitian ini bertujuan untuk menganalisis hubungan cadangan devisa, jumlah uang beredar, dan net ekspor terhadap nilai tukar rupiah di Indonesia. Data yang digunakan dalam penelitian adalah data sekunder selama periode Tahun 1988 – 2019. Alat analisis dgunakan ialah metode Vector Auto Regression (VAR). Hubungan beredar tidak sgnifikan Variabel membutuhkan waktu empat sampai delapan tahun kemb...

Decision models under uncertainty need to be feeded with scenarios of the interest rate curve. Such scenarios have to comply, as close as possible, with the empirical distribution of each rate. Simulation models of the term structure usually assume that the conjugate distribution of the interest rates is lognormal. Dynamic models, like vector auto-regression, implicitly postulate that the logar...