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Fuzzy dispersion entropy (FuzDE) is a newly proposed metric, which combines the superior characteristics of fuzzy (FE) and (DE) in signal analysis. However, FuzDE only reflects feature from original signal, ignores hidden information on time scale. To address this problem, we introduce variable-step multiscale processing propose (VSMFuzDE), realizes characterization abundant scale information, ...

Nearest-neighbour interpolation algorithms have many useful properties for applications to learning, but they often exhibit poor generalization. In this paper, it is shown that much better generalization can be obtained by using a variable interpolation kernel in combination with conjugate gradient optimization of the similarity metric and kernel size. The resulting method is called variable-ke...

A new family of limited-memory variable metric or quasi-Newton methods for unconstrained minimization is given. The methods are based on a positive definite inverse Hessian approximation in the form of the sum of identity matrix and two low rank matrices, obtained by the standard scaled Broyden class update. To reduce the rank of matrices, various projections are used. Numerical experience is e...

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most powerful evolutionary algorithms for real-valued single-objective optimization. Here a variant of the CMA-ES for multi-objective optimization (MOO) is developed. First a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule is introduced. This algorithm is compared to th...

An algorithm for stochastic (convex or nonconvex) optimization is presented. The algorithm is variable-metric in the sense that, in each iteration, the step is computed through the product of a symmetric positive definite scaling matrix and a stochastic (mini-batch) gradient of the objective function, where the sequence of scaling matrices is updated dynamically by the algorithm. A key feature ...

A new family of limited-memory variationally-derived variable metric or quasi-Newton methods for unconstrained minimization is given. The methods have quadratic termination property and use updates, invariant under linear transformations. Some encouraging numerical experience is reported.

In this report we propose a new recursive matrix formulation of limited memory variable metric methods. This approach enables to approximate of both the Hessian matrix and its inverse and can be used for an arbitrary update from the Broyden class (and some other updates). The new recursive formulation requires approximately 4mn multiplications and additions for the direction determination, so i...