a comparative approximate economic behavior analysis of support vector machines and neural networks models

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

A Comparative Approximate Economic Behavior Analysis of Support Vector Machines and Neural Networks Models

he application of the artificial neural networks in economics and business goes back to 1950s, while the main part of the applications has been developed in more recent years. Reviewing this research indicates that the development and applications of neural network are not limited to a specific application area as it spans a wide variety of fields from prediction to classification, as most of t...

Comparative Exudate Classification Using Support Vector Machines and Neural Networks

After segmenting candidate exudates regions in colour retinal images we present and compare two methods for their classification. The Neural Network based approach performs marginally better than the Support Vector Machine based approach, but we show that the latter are more flexible given criteria such as control of sensitivity and specificity rates. We present classification results for diffe...

Credit rating analysis with support vector machines and neural networks: a market comparative study

Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory p...

A Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels

The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...

Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data

This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values.  Seismic surveying was performed next on these models. F...