The classification of normal and malginant colon tissue cells is crucial to the diagnosis of colon cancer in humans. Given the right set of feature vectors, Support Vector Machines (SVMs) have been shown to perform reasonably well for the classification [4, 13]. In this paper, we address the following question: how does the choice of a kernel function and its parameters affect the SVM classific...

Multi-category classification is an on going research topic in image acquisition and processing for numerous applications. In this paper, a novel approach called margin and domain integrated classifier (MDIC) is addressed. It merges the conventional support vector machine (SVM) and support vector domain description (SVDD) classifiers, and handles multi-class problems as a combination of several...

In today’s world, we can say that information and its processing has become the critical aspect for functioning of everything. In the early days, information was generally obtained and processed in the form of text. Today information is available in all forms namely, text, music, graphics, etc. which are a easily understandable and accurately represent information. Information is first captured...

In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed ...

The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels. We show on a digit recognition task that the proposed approach ...

We propose a method to make a relationship between images and words. We adopt two processes in the method, one is a process to uniformly divide each image into sub-images with key words, and the other is a process to carry out vector quantization of the sub-images. These processes lead to results which show that each sub-image can be correlated to a set of words each of which is selected from w...

Detection of Alzheimer's disease over brain Magnetic Resonance Imaging (MRI) data is a priority goal in the Neurosciences. In previous works we have studied the accuracy of feature vectors obtained from VBM studies of the MRI data. In this paper we report results working on deformation based features, obtained from the deformation vectors computed by non-linear registration processes. Feature s...

This paper describes a new technique for statistical 3{D object localization. Local feature vectors are extracted for all image positions, in contrast to seg-mentation in classical schemes. We deene a density function for those features and describe a hierarchical pose estimation scheme for the localization of a single object in a scene with arbitrary background. We show how the global pose sea...

in the last two decade the use of aerial laser scanner (als) or lidar (light detection and ranging) sensor in geomatics engineering and surveying application has augmented significantly . the main reason of the mentioned phenomenon is the reliability and accuracy of the data obtained by lidar sensors. the output of lidar is unclassified 3d point cloud. classification of the lidar point clouds i...

Choosing appropriate classification algorithms for a given data set is very important and useful in practice but also is full of challenges. In this paper, a method of recommending classification algorithms is proposed. Firstly the feature vectors of data sets are extracted using a novel method and the performance of classification algorithms on the data sets is evaluated. Then the feature vect...