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A topological and dynamical characterization of the cluster structures described by the support vector clustering is developed. It is shown that each cluster can be decomposed into its constituent basin level cells and can be naturally extended to an enlarged clustered domain, which serves as a basis for inductive clustering. A simplified weighted graph preserving the topological structure of the clusters is also constructed and is employed to develop a robust and inductive clustering algorithm.

Financial models with stochastic volatility or jumps play a critical role as alternative option pricing models for the classical Black–Scholes model, which have the ability to fit different market volatility structures. Recently, machine learning models have elicited considerable attention from researchers because of their improved prediction accuracy in pricing financial derivatives. We propose a generative Bayesian learning model that incorporates a prior reflecting a risk-neutral pricing stru

This article describes an evaluation of the mechanical properties and sound insulation effects of composites made of acrylonitrile butadiene styrene (ABS) and carbon-black using the design of experiment (DOE) approach. The solution blending process and method are presented. The effect of the acetone content in ABS during the drying process was studied by conducting tensile tests of injection-molded specimens. ABS was dissolved in acetone, and carbon-black was dispersed in the ABS/acetone mixture

Abstract The shocks on certain market spread to other markets due to the financial linkages of global economy, which is known as volatility spillover effect. In this study, we propose a volatility forecasting model for global market indices using the spatial‐temporal graph neural network (GNN). The volatility spillover between markets are reflected in the model by estimating the linkage between markets, which is the input of GNN, using the volatility spillover index. An empirical analysis is con

Keystroke authentication is a biometric method utilizing the typing characteristics of users. In this paper, we propose an evolutionary method for stable keystroke authentication. In the method, typing characteristics of users are represented by n-dimensional vectors and an ellipsoidal hypothesis space, which distinguishes a collection of the timing vectors of a user from those of the others, is evolved by a genetic algorithm. A filtering scheme and an adaptation mechanism are also presented to

The reflected gradient method and the Newton trajectory method are approaches to compute the closest unstable equilibrium point (UEP) for stability region estimation. We address the computational issues involved in these methods. We first suggest a dynamic gradient approach as a unified and extended version of these methods. Then, we show that computing the closest UEP using the dynamic gradient approach can be infeasible.