Support Vector Classier with Asymmetric Kernel Functions
نویسنده
چکیده
In support vector classi er, asymmetric kernel functions are not used so far, although they are frequently used in other kernel classi ers. The applicable kernels are limited to symmetric semipositive de nite ones because of Mercer's theorem. In this paper, SVM is extended to be applicable to asymmetric kernel functions. It is proven that, when a positive de nite kernel is given, the extended SVM is identical with the conventional SVM. In the 3D object recognition experiment, the extended SVM with asymmetric kernels performed better than the conventional SVM.
منابع مشابه
MODELING OF FLOW NUMBER OF ASPHALT MIXTURES USING A MULTI–KERNEL BASED SUPPORT VECTOR MACHINE APPROACH
Flow number of asphalt–aggregate mixtures as an explanatory factor has been proposed in order to assess the rutting potential of asphalt mixtures. This study proposes a multiple–kernel based support vector machine (MK–SVM) approach for modeling of flow number of asphalt mixtures. The MK–SVM approach consists of weighted least squares–support vector machine (WLS–SVM) integrating two kernel funct...
متن کاملRemote 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 کاملSeparating 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 کاملParallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers
This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help u...
متن کاملA novel optimization parameters of support vector machines model for the land use/ cover classification
Nowadays, support vector machines (SVM) are receiving increasing attention in land cover/use classification although one of the major drawbacks of the technique is the kernel function selection and its parameters setting. In this paper, a novel SVM parameters optimization method based on selfadaptive mutation particle swarm optimizer (SAMPSO-SVM) is proposed to improve the generalization perfor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998