Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM(+) remote sensing image. This algorithm is applied to extract various types of lands of the city Da'an in northern China. Two multi-category strategies, namely "one-against-one" and "o...

in this paper, a maximum likelihood estimation and a minimum entropy estimation for the expected value and variance of normal fuzzy variable are discussed within the framework of credibility theory. as an application, a credibilistic portfolio selection model is proposed, which is an improvement over the traditional models as it only needs the predicted values on the security returns instead of...

The Maximum Likelihood Sequence Estimator (MLSE) and the Bayesian detector provide the best performance (at BER sense) under a data sequence or a symbol-by-symbol detection philosophy, respectively [1]. However, these detectors present a high computational burden and are quite unaffordable for a mobile terminal that exploits also the spatial diversity. To overcome this problem Fuzzy Logic has b...

The paper presents an automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy s...

In this paper, a maximum likelihood estimation and a minimum entropy estimation for the expected value and variance of normal fuzzy variable are discussed within the framework of credibility theory. As an application, a credibilistic portfolio selection model is proposed, which is an improvement over the traditional models as it only needs the predicted values on the security returns instead of...

In this paper, we evaluate and contrast two fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimisation and boosting whereas the second classifier is based on a fuzzy neural network. We show that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5 algorithm that we included as a ref...

Conventional image classification methods restricts each pixel of data set to exclusively just one cluster. As a consequence, with this approach the classification results are often very crispy, i.e., each pixel of the image belongs to exactly just one class. However, in many real situations, for images, issues such as limited spatial resolution, poor contrast, overlapping intensities, and nois...

This paper deals with modulation classification, First, a state of the art is given which is separated into two classes: the pattern recognition approach and the Maximum Likelihood (ML) approach. Then we propose a new classifier called the General Maximum Likelihood Classilier (GMLC) based on an approximation of the likelihood function. We derive equations of this classifier in the case of line...