Neural networks have been successfully applied in a wide range of supervised and unsuper-vised learning applications. Neural-network methods are not commonly used for data-mining tasks, however, because they often produce incomprehensible models and require long training times. In this article, we describe neural-network learning algorithms that are able to produce comprehensible models, and th...

In general, there are two ways to deal with one-against-all multiclass neural network classification. The first way is the use of a single k-class neural network trained with multiple outputs. Another way is the use of multiple binary neural networks. This paper focuses on the later way in which multiple complementary neural networks are applied to one-against-all instead of using only multiple...

This paper, recognize of the patterns using spiking neural networks with temporal encoding and learning. Neural networks place the important role in cognitive and decision making process. Processing the different type of inputs lead to find the discriminate the pattern. Leaky Integrate Fire Neurons are used to recognize the patterns. During the recognition supervised learning method is used to ...

life style and life expectancy of inhabitants have been affected by the increase of particulate matter 10 micrometers or less in diameter (pm10) in cities and this is why maximum pm10 concentrations have received extensive attention. an early notice system for pm10 concentrations necessitates an accurate forecasting of the pollutant. in the current study an artificial neural network was used t...

Online social networks like Instagram are places for communication. Also, these media produce rich metadata which are useful for further analysis in many fields including health and cognitive science. Many researchers are using these metadata like hashtags, images, etc. to detect patterns of user activities. However, there are several serious ambiguities like how much reliable are these informa...

This section concerns neural networks which are hybrid either in terms of structure or in terms of training algorithms. The counterpropagation network is one that incorporates structural characteristics of the Kohonen and Grossberg networks and it is trained by composite supervised–unsupervised methods. The adaptive critic concept concerns neural network implementations of reinforcement learnin...

Free swelling index (FSI) is an important parameter for cokeability and combustion of coals. In this research, the effects of chemical properties of coals on the coal free swelling index were studied by artificial neural network methods. The artificial neural networks (ANNs) method was used for 200 datasets to estimate the free swelling index value. In this investigation, ten input parameters ...

Neural networks have been successfully applied in a wide range of supervised and unsuper vised learning applications Neural network methods are not commonly used for data mining tasks however because they often produce incomprehensible models and require long training times In this article we describe neural network learning algorithms that are able to produce comprehensible models and that do ...

This study focused on development and application of efficient algorithm for clustering and classification of supervised visual data. In machine learning clustering classification and clustering is most useful techniques in pattern recognition and computer vision. Existing techniques for subspace clustering makes use of rank minimization and spars based that are computationally expensive and ma...