This paper presents a global methodology to build a nonlinear regression when the number of available samples is small compared to the number of inputs. The task is divided in two parts: selection of the best inputs and construction of the approximator. A first SOM is used to compute clean correlations between the inputs and the output. A second SOM is built to link the output to the selected i...

For the model of communication through a discrete memoryless channel using i.i.d. random block codes, where the channel is changing slowly from block to block, we propose a stochastic algorithm for adaptation of the generating distribution of the code in the process of continuous reliable communication. The purpose of the algorithm is to match the generating distribution Q(x) to the changing ch...

Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In t...

In this work we present a biologically motivated framework for the modeling of the visual scene exploration preference. We aim at capturing the statistical patterns that are elicited by the subjective visual selection and reproduce them via a computational system.

the stock evaluation process plays an important role in portfolio selection because it is the prerequisite for investment and directly influences on the stock allocation. this paper presents a methodology based on data envelopment analysis for portfolio selection, decision making units which can be stocks or other financial assets. first, dmus efficiencies are computed based on input/output com...

In this paper we will treat input selection for a radial basis function (RBF) like classifier within a Bayesian framework. We approximate the a-posteriori distribution over both model coefficients and input subsets by samples drawn with Gibbs updates and reversible jump moves. Using some public datasets, we compare the classification accuracy of the method with a conventional ARD scheme. These ...

This paper compares two neural network input selection schemes, the Principal Component Analysis (PCA) and the Automatic Relevance Determination (ARD) based on MacKay's evidence framework. The PCA takes all the input data and projects it onto a lower dimension space, thereby reducing the dimension of the input space. This input reduction method often results with parameters that have significan...

Introduction: Amajor problem in the treatment of cancer is the lack of an appropriate method for the early diagnosis of the disease. The chemical reaction within an organ may be reflected in the form of proteomic patterns in the serum, sputum, or urine. Laser mass spectrometry is a valuable tool for extracting the proteomic patterns from biological samples. A major challenge in extracting such ...

different industrial decision makers and managers in our country, try to select and organize locations for aggregating industrial units, estates and areas with respect to land use planning visions and industrial development strategies. in this regard, considering large quantity of the input data and diverse criteria affecting this application, it is complicated and difficult to optimally make d...

one of the most important steps involved in mining operations is to select an appropriate extraction method for mine resources. after choosing the extraction method, it is usually impossible to replace it with another one because it may be so expensive that implementation of the entire project could be economically impossible. choosing a mining method depends on the geological and geometrical c...