Twin Support Vector Machines Based on the Mixed Kernel function

نویسندگان

  • Fulin Wu
  • Shifei Ding
چکیده

The efficiency and performance of the Twin Support Vector Machines (TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel functions. Generally, TWSVM selects the Gaussian radial basis kernel function. Although it has a strong learning ability, its generalization ability is relatively weak. In a certain extent, this will limit the performance of TWSVM. In order to solve the problem of selecting kernel functions in TWSVM, we propose the twin support vector machines based on the mixed kernel function (MK-TWSVM) in this paper. To make full use of the learning ability of local kernel functions and the excellent generalization ability of global kernel functions, MK-TWSVM selects a global kernel function and a local kernel function to construct a mixed kernel function which has the better performance. The experimental results indicate that the mixed kernel function makes TWSVM have the good learning ability and generalization ability. So it improves the performance of TWSVM.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Mixed Kernel Twin Support Vector Machines Based on the Shuffled Frog Leaping Algorithm

The efficiency and performance of Twin Support Vector Machines (TWSVM) is better than the traditional support vector machines when it deals with the problems. However, it also has some problems. As the same as the traditional support vector machines, its parameters are difficult to be appointed and it is not easy to select the appropriate kernel function. TWSVM generally selects the Gaussian ra...

متن کامل

Invasive Weed Optimization Algorithm for Optimizating the Parameters of Mixed Kernel Twin Support Vector Machines

How to select the suitable parameters and kernel model is a very important problem for Twin Support Vector Machines (TSVMs). In order to solve this problem, one solving algorithm called Invasive Weed Optimization Algorithm for Optimizating the Parameters of Mixed Kernel Twin Support Vector Machines (IWO-MKTSVMs) is proposed in this paper. Firstly, introducing the mixed kernel, the twin support ...

متن کامل

Separating Well Log Data to Train Support Vector Machines for Lithology Prediction in a Heterogeneous Carbonate Reservoir

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...

متن کامل

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...

متن کامل

Remote Sensing and Land Use Extraction for Kernel Functions Analysis by Support Vector Machines with ASTER Multispectral Imagery

Land use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, cost effective data, map accurate data, and large archives of historical data. To study land use / cover, remote sensing as an effic...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • JCP

دوره 9  شماره 

صفحات  -

تاریخ انتشار 2014