Sulan Zhang

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This paper proposes an approach to derive a parametric L-system in parallel based on Compute Unified Device Architecture (CUDA). It consists of a host program running on CPU and a device program running on CUDA enabled GPU. The host program is used to transfer data between CPU and GPU, pre-allocate host and device memory, and launch the device program. The(More)
The naive Bayesian classifier (NBC) is a simple yet very efficient classification technique in machine learning. But the unpractical condition independence assumption of NBC greatly degrades its performance. There are two primary ways to improve NBC's performance. One is to relax the condition independence assumption in NBC. This method improves NBC's(More)
This paper discusses the application of support vector machine (SVM) in stock price change trend forecasting. By reviewing prior research, thirteen technical indicators are defined as the input attributes of SVM. By training this model, we can forecast if the stock price would rise the next day. In order to make best use of market information, analyst(More)
We propose an appearance-based image clustering approach called GGCI (global geometric clustering for image). For face images taken with varying pose, expression, eyes (wearing sunglasses or not) or object images under different viewing conditions, GGCI uses easily measured local metric information to learn the underlying global geometry of images space,(More)
We propose two geometric structure based approaches GGCI (global geometric clustering for image) and GSIM (geometric structure based image matching) for image clustering and image matching, respectively. For face images or object images taken with varying factors, the GGCI approach learns the global geometric structure of images space and clusters images(More)
Automatic subject indexing is a process to produce automatically a set of attributes that represent the content or topic of a document. In this paper, two approaches of automatic subject indexing based on VSM (vector space model) and subject words segmentation respectively are presented. The experimental results show that the first approach based on VSM is(More)
Traditional outlier mining methods identify outliers from a global point of view. These methods are inefficient to find locally-biased data points (outliers) in low dimensional subspaces. Constrained concept lattices can be used as an effective formal tool for data analysis because constrained concept lattices have the characteristics of high constructing(More)