Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to impro...

We are developing a pixel-level cloud-type classifier for the Multi-angle Imaging SpectroRadiometer (MISR), an instrument used to study clouds and aerosols from NASA’s Terra satellite. To augment MISR’s existing high-level products (including cloud masks, cloud heights, and aerosol optical depth retrievals), our cloudtype classifier labels each 1.1-km pixel as clear, or as belonging to one of s...

Support vector machines (SVM) have been promising methods for classification because of their solid mathematical foundations which convey several salient properties that other methods hardly provide. However, despite of the prominent properties of SVM, they are not as favored for large-scale data as complexity of SVM is highly dependent on the size of a data set. Microarray gene expression data...

This paper proposes a new method for extracting impervious surface from VHR imagery. Since the impervious surface is the only class of interest (i.e. target class), the One Class Support Vector Machine (OCSVM), a recently developed statistical learning method, was used as the classifier. Rather than use samples from all classes for training in traditional multi-class classification, the method ...

Feature extraction, discriminant analysis, and classification rule are three crucial issues for face recognition. This paper presents one method, named GaborfaceSVM, to handle three issues together. For feature extraction, we utilize the Gabor wavelet transform on grey face image to extract Gaborfaces. A Modified Enhanced Fisher Discriminant model is used to reinforce discriminant power of Gabo...

Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a spe...

How to acquire new knowledge from new added training data while retaining the knowledge learned before is an important problem for incremental learning. In order to handle this problem, we propose a novel algorithm that enables support vector machines to accommodate new data, including samples that correspond to previously unseen classes, while it retains previously acquired knowledge. Furtherm...

The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions data points. Recently, multilevel approaches to train SVMs have been developed allow for time-efficient datasets. While regular perform the entire in one—time-consuming—optimization step, first build a hierarchy problems decreasing size that resemble original problem and then an SVM model eac...