We show that any p-selective and self-reducible set is in P. As the converse is also true, we obtain a new characterization of the class P. A generalization and several consequences of this theorem are discussed. Among other consequences, we show that under reasonable assumptions auto-reducibility and self-reducibility diier on NP, and that there are non-p-T-mitotic sets in NP.

In this research paper, we use a normalized graph cut measure as a thresholding principle to separate an object from the background based on the standard S membership function. The implementation of the proposed algorithm known as fuzzy normalized graph cut method. This proposed algorithm compared with the fuzzy entropy method [25], Kittler [11], Rosin [21], Sauvola [23] and Wolf [33] method. M...

This paper generalizes the concepts of rough membership functions in pattern classification tasks to fuzz rough membership functions. Unlike the rough membersgp value of a pattern, which is sensitive only towards the rough uncertainty associated with the pattern, the fuzzy-rough membership value of the pattern signlfies the rou h uncertainty as well as the . fuzz uncertainty associated wig it. ...

Fuzzy regression models has been traditionally considered as a problem of linear programming. The use of quadratic programming allows to overcome the limitations of linear programming as well as to obtain highly adaptable regression approaches. However, we verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in order to address this prob...

A margin based feature selection approach is explored for hyperspectral data. This approach is based on measuring the confidence of a classifier when making predictions on a test data. Greedy feature flip and iterative search algorithms, which attempts to maximise the margin based evaluation functions, were used in the present study. Evaluation functions use linear, zero-one and sigmoid utility...

This work contributes to the development of a new data-driven method (D-DM) feedforward neural networks (FNNs) learning. was proposed recently as way improving randomized learning FNNs by adjusting network parameters target function fluctuations. The employs logistic sigmoid activation functions for hidden nodes. In this study, we introduce other functions, such bipolar sigmoid, sine function, ...

In this paper we study some properties related to the distribution of membership grades for non-stationary fuzzy sets. We obtain the formulation for the distribution, where the non-stationary fuzzy sets are obtained by generating instantiations about the center values. The two cases considered are for the underlying membership functions as Triangular and Gaussian. The analytical results obtaine...