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 work investigates the use of multi dimensional histograms for segmentation of images of chronic wounds. We employ a Support Vector Machine (SVM) classifier for automatic extraction of wound region from an image. We show that the SVM classifier can generalize well on the difficult wound segmentation problem using only 3-D dimensional color histograms. We also show that color histograms of hi...

As an indispensable defensive measure of network security, the intrusion detection is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents. It is a classifier to judge the event is normal or malicious. The information used for intrusion detection contains some redundant features which would increase the difficulty of tra...

In this paper, two classifiers are proposed to distinguish between bulking and nonbulking situations in an activated sludge wastewater treatment plant, based on available image analysis information. The first classifier consists of a simple linear classification function, while the second classifier uses a highly nonlinear least squares support vector machine (LS-SVM) to distinguish between bot...

We propose Sparse TSVM, a multi-class SVM classifier that determines k nonparallel planes by solving k related SVM-type problems. The Sparse TSVM promotes Twin SVM to one-versus-rest approach. And it capture classes' main feature better with the sparse algorithm. On several benchmark data sets, Sparse TSVM is not only fast, but shows good generalization.

An evaluation of using a support vector machine (SVM) to classify operating system fingerprints in the Nmap security scanner. In solving a simplified version of operating system classification, the SVM got marginally more accurate results than Nmap’s built-in classifier.

in this paper, a smart method is designed in order to classify healthy and illness ducks using their emission voice. for this purpose, firstly, the birds based on their healthy condition are divided into the different categories and then their voices are saved using a microphone and data acquisition card. gained signals were transformed from time-domain signal to frequency domain using fast fou...

Mammograms are one of the most widely used techniques for preliminary screening of breast cancers. There is great demand for early detection and diagnosis of breast cancer using mammograms. Texture based feature extraction techniques are widely used for mammographic image analysis. In specific, wavelets are a popular choice for texture analysis of these images. Though discrete wavelets have bee...

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition and machine learning, however, as a similar lazy classifier using local information for recognizing a new test, neighborhood classifier, few literatures are reported on. In this paper, we introduce neighborhood rough set model as a uniform framework to understand and implement neighborhood classifiers. T...

We developed a pedestrian classifier using GFB(Gabor Filter Bank)-based feature extraction and SVM(Support Vector Machine). Because the SVM uses RBF(Radial Basis Function) and is applied for nonseparable data, learning parameters should be optimized. This paper proposes GA(Ganetic Algorithm)-based optimization of SVM learning parameters.