Lagrangian Support Vector Machines

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...

Mining Biological Repetitive Sequences Using Support Vector Machines and Fuzzy SVM

Structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. Biggest class of the repetitive subsequences is “Transposable Elements” which has its own sub-classes upon contexts’ structures. Many researches have been performed to criticality determine the structure and function of repetitiv...

STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P

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

Data Mining via Support Vector Machines

Support vector machines (SVMs) have played a key role in broad classes of problems arising in various fields. Much more recently, SVMs have become the tool of choice for problems arising in data classification and mining. This paper emphasizes some recent developments that the author and his colleagues have contributed to such as: generalized SVMs (a very general mathematical programming framew...

Face Recognition using Eigenfaces , PCA and Supprot Vector Machines

This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...