, that the iterative improvement approach is generic in that it is possible

and to networks used to classify handprinted numeral image data. The improvement of the iterative technique over classical de-sign approaches is clearly demonstrated.

The trigonometric network, introduced in this paper, is a multilayer feedforward neural network with sinusoidal activation functions. Unlike the N-dimensional Fourier series, the basis functions of the proposed trigonometric network have no strict harmonic relationship. An effective training algorithm for the network is developed. It is shown that the trigonometric network performs better than the sigmoidal neural network for some data sets. A pruning method based on the modified Gram-Schmidt orthogonalization procedure is presented to detect and prune useless hidden units. Other network architectures related to the trigonometric network, such as the sine network, are shown to be inferior to the network proposed in this paper. I. Introduction The trigonometric feedforward neural network proposed in this paper has both sine and cosine activations for each hidden layer net function. Since the derivative of the activations for the net function are never simultaneously zero, the hope is...

ABSTRACT. We present a new algorithm for a fast iterative improvement of the shallow-ice approximation (SIA) for the modeling of glacier flow. Based on the traditional SIA scaling assumptions, the solution of the Stokes problem is found by an operator-splitting iterative technique. The SIA solution

Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate

An iterative mincut heuristic for partitioning networks is presented whose worst case computation time, per pass, grows linearly with the size of the network. In practice, only a very small number of passes are typically needed, leading to a fast approximation algorithm for mincut partitioning

The accuracy of multiple sequence alignment pro-gram MAFFT has been improved. The new version (5.3) of MAFFT offers new iterative refinement options, H-INS-i, F-INS-i and G-INS-i, in which pairwise alignment information are incorporated into objective function. These new options of MAFFT showed

Due to complexity reasons of realistic scheduling applications, often iterative improvement techniques that perform a kind of local search to improve a given schedule are proposed instead of enumeration techniques that guarantee optimal solutions. In this paper we describe an experimental

with a broad overview of the program. Important operations are given priority, no matter what their source block was. This is in sharp contrast with earlier methods, which compact one block at a time and then attempt iterative improvement. It is argued that those methods suffer from the lack

New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the million queens. However, on highly constrained problems it is possible for these methods to get caught in local minima