The work examines Karhunen-Loeve Transform and Linear Discriminant Analysis as means for designing optimized spectral bases for the projection of the critical-band auditory-like spectrum.

A basis of symmetric functions, which we denote by q*(X; q, t), was introduced in the work of Ram and King and Wybourne in order to describe the irreducible characters of the Hecke algebras of type A. In this work we give combinatorial descriptions of the expansions of the functions q*(X; q, t) in terms of the classical bases of symmetric functions and apply these results in determining the det...

Considerable progress was recently made on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabeled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper suggests a space of basis functions to perform semi-supervised inductive learning. As a nice pr...

Since there is no individual approach that can be universally applied to effectively solve the hard problems of artificial intelligence and data analysis, hybrid systems are necessary to better tackle specific tasks by exploiting the advantages of different methodologies in a single framework. Based on known results of combining neural networks and rule-based systems, this work presents a hybri...

We use the weighted integral form of spherical Bessel functions, and introduce a new analytical set of complete and biorthogonal potential–density basis functions. The potential and density functions of the new set have finite central values and they fall off, respectively, similar to r and r at large radii where l is the latitudinal quantum number of spherical harmonics. The lowest order term ...

Radial basis functions are tools for reconstruction of mul-tivariate functions from scattered data. This includes, for instance, reconstruction of surfaces from large sets of measurements, and solving partial diierential equations by collocation. The resulting very large linear N N systems require eecient techniques for their solution, preferably of O(N) or O(N log N) computational complexity. ...

Based on the approach suggested by Tarantola, and Gauthier et al., we show that the alternate use of the step (linear) function basis and the block function (quasi-delta function) basis can give accurate full waveform inversion results for the layered acoustic systems, starting from a uniform background. Our method is robust against additive white noise (up to 20% of the signal) and can resolve...

Radial basis function (RBFs) neural networks provide an attractive method for high dimensional nonparametric estimation for use in nonlinear control. They are faster to train than conventional feedforward networks with sigmoidal activation networks (\backpropagation nets"), and provide a model structure better suited for adaptive control. This article gives a brief survey of the use of RBFs and...

Filamin A (FLNa) can effect orthogonal branching of F-actin and bind many cellular constituents. FLNa dimeric subunits have N-terminal spectrin family F-actin binding domains (ABDs) and an elongated flexible segment of 24 immunoglobulin (Ig) repeats. We generated a library of FLNa fragments to examine their F-actin binding to define the structural properties of FLNa that enable its various func...

Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea of the approach is to approximate the optimal value function by a set of basis functions and optimize their weights by linear programming. The quality of this approximation naturally depends on its basis functions. How...