today a significant part of available data is saved in text database or text documents. the most important thing is to organize these documents. one way to organize text documents is to classify them. to classify texts is to assign text documents to their actual categories. this has two main steps, i.e. feature- and learning algorithm selection. there have been several methods suggested to clas...

Applying nonlinear models to estimation and forecasting economic models are now becoming more common, thanks to advances in computing technology. Artificial Neural Networks (ANN) models, which are nonlinear local optimizer models, have proven successful in forecasting economic variables. Most ANN models applied in Economics use the gradient descent method as their learning algorithm. However, t...

This paper describes a method which, under certain circumstances, allows to automatically learn or adjust similarity measures. For this, ideas of connectionist learning procedures, in particular those related to Hebbian learning, are combined with a Case-Based Reasoning engine.

Spatial and contextual learning are considered to be dependent on the hippocampus, but the extent to which other structures in the medial temporal lobe memory system support these functions is not well understood. This study examined the effects of individual and combined lesions of the perirhinal, postrhinal, and entorhinal cortices on spatial and contextual learning. Lesioned subjects were co...

With lot of research and advancement of deep learning, complex unsupervised learning is applied for extracting deep hierarchies of features especially to images. But, off-the-shelf unsupervised learning algorithms combined with deep learning techniques would yield results similar to complext,time consuming Deep learning algorithms. In this report, I would use K-means algorithm based on [1][3] a...

This paper proposes an effective multi-objective differential evolution algorithm (MDES) to solve a permutation flow shop scheduling problem (PFSSP) with modified Dejong's learning effect. The proposed algorithm combines the basic differential evolution (DE) with local search and borrows the selection operator from NSGA-II to improve the general performance.  First the problem is encoded with a...