Learn More
Developing a system which generates a 3D representation of a whole scene is a difficult task. Several new technologies of 3D time-of-flight (ToF) imaging, which overcome various limitations of other 3D imaging systems, such as laser/radar/sonar scanners, structured light and stereo rigs have been developed in recent years. However, only limited work got(More)
Selection of significant genes via expression patterns is an important problem in microarray data processing. In this article, we propose and study a new method for selecting relevant genes obtained by spectral biclustering and based on similarity between genes and eigenvectors. The proposed algorithm can select a much smaller gene subset to make accurate(More)
A method which we call support vector machine with graded resolution (SVM-GR) is proposed in this paper. During the training of the SVM-GR, we first form data granules to train the SVM-GR and remove those data granules that are not support vectors. We then use the remaining training samples to train the SVM-GR. Compared with the traditional SVM, our SVM-GR(More)
In this paper, we investigate the linear solver in least square support vector machine (LSSVM) for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equations should be solved repeatedly for choosing appropriate parameters in LSSVM, so the key for speeding up LSSVM is to improve the method of(More)
In this paper, after a brief overview of the existing methods, we present a new hierarchical classification algorithm based on quotient space theory of the granular computing. This algorithm deals with the samples from coarse to fine both in the training and testing processes. A group of classifiers are firstly trained by the samples generated under(More)
  • 1