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Sparse coding has received considerable research attentions due to its competitive performance for SPM-based image classification algorithms. In sparse coding, each low-level image descriptor (e.g., SIFT) is quantized into a sparse vector using an over-complete dictionary. Two typical schemes for achieving the code sparsity are imposing l1-sparsity penalty on the coding coefficients, or selecting...
Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service...
Transfer learning targets to leverage knowledge from one domain for tasks in a new domain. It finds abundant applications, such as text/sentiment classification. Many previous works are based on cluster analysis, which assume some common clusters shared by both domains. They mainly focus on the one-to-one cluster correspondence to bridge different domains. However, such a correspondence scheme might...
The exponential growth of Web images has created a compelling need for innovative methods to retrieve and manage them. Automatic image annotation is an effective way for resolving this problem. In this paper, we propose a novel system that automatically annotates images by semantic corpus which is constructed by mining Web image data. It includes three parts: 1) constructing the semantic annotation...
In this paper, a supremum of the total numbers of fuzzy rules is obtained by employing the characteristics of the function in building a fuzzy model to approximate the function to achieve a given approximation accuracy. The basic idea is to take advantage of the presentation of mean square error to formulate the supremum of the total numbers of fuzzy rules. The supremum relates approximation accuracy,...
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