One way of using entropy criteria in learning systems is to minimize the entropy of the error between the output of the learning system and the desired targets. In our last work, we introduced the Error Entropy Minimization (EEM) algorithm for neural network classification. There are some sensible aspects in the optimization of the EEM algorithm: the size of the Parzen Window (smoothing paramet...

We generalize Gaspard's method for computing the ε-entropy production rate to dissipative systems with attractors. This approach leads to a natural definition of a coarse grained Gibbs entropy which is extensive, and which can be expressed in terms of the SRB measures and volumes of the coarse graining sets which cover the attractor. One can also study the entropy and entropy production as func...

The dispersed domains which result from phase separation in phospholipid monolayers have long been known to exhibit complex and intriguing geometries. Over the last decade, much work has gone into the theoretical prediction of these shapes using energy minimization calculations. While such studies have provided much insight into the behavior of domain shapes, they ignore the effect of entropy a...

Fuzzy c-means (FCM) is an important clustering algorithm. However, it does not consider the impact of different feature on clustering. In this paper, we present a fuzzy clustering algorithm with the generalized entropy of feature weights FCM (GEWFCM). By introducing feature weights and adding regularized term of their generalized entropy, a new objective function is proposed in terms of objecti...

A novel global search algorithm based method is proposed to separate MR images blindly in this paper. The key point of the method is the formulation of the new matrix which forms a generalized permutation of the original mixing matrix. Since the lowest entropy is closely associated with the smooth degree of source images, blind image separation can be formulated to an entropy minimization probl...

generally, exponential Lévy models to a finite set of observed option prices. We show that the usual formulations of the inverse problem via non-linear least squares are ill-posed and propose a regularization method based on relative entropy: we reformulate our calibration problem into a problem of finding a risk-neutral exponential Lévy model that reproduces the observed option prices and has ...

In statistical learning the excess risk of empirical risk minimization (ERM) is controlled by ( COMPn(F) n )α , where n is a size of a learning sample, COMPn(F) is a complexity term associated with a given class F and α ∈ [ 1 2 , 1] interpolates between slow and fast learning rates. In this paper we introduce an alternative localization approach for binary classification that leads to a novel c...