This paper describes attribute extraction, attribute classification and data cleaning process in tourism emergency, builds a widely applicable decision table, and uses the attribute reduction algorithm based on the im...
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Based on geographical and semantic features of remote sensing image, the paper presents a universal remote sensing image data model, and analyzes the logical structure and storage methods of this model. Based on the m...
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Document classification is one of important steps in document mining. In this paper, we present a new kind of document classification method based on generalized learning model (GLM for short). GLM is an extensible ma...
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Document classification is one of important steps in document mining. In this paper, we present a new kind of document classification method based on generalized learning model (GLM for short). GLM is an extensible machine learning model with great flexibility. It may fuses symbolic learning, fuzzy learning, statistical learning, and neural learning together. If necessary, new learning model can be incorporated. To describe and represent documents more reasonably, we develop a approach to extract membership vector as features of documents. In view of the characteristics of document classification, two kinds of document classification methods are employed under GLM frame. One is based on fuzzy set theory, the other is based on support vector machine (SVM). These two kinds of methods can supplement each other to achieve better performance.
Location recommendation is an important feature of social network applications and location-based services. Most existing studies focus on developing one single method or model for all users. By analyzing real locatio...
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Location recommendation is an important feature of social network applications and location-based services. Most existing studies focus on developing one single method or model for all users. By analyzing real location-based social networks, in this paper we reveal that the decisions of users on place visits depend on multiple factors, and different users may be affected differently by these factors. We design a location recommendation framework that combines results from various recommenders that consider various factors. Our framework estimates, for each individual user, the underlying influence of each factor to her. Based on the estimation, we aggregate suggestions from different recommenders to derive personalized recommendations. Experiments on Foursquare and Gowalla show that our proposed method outperforms the state-of the-art methods on location recommendation.
We present a particle swarm optimization algorithm OT-PSO using orthogonal test technique. Based on the classical PSO, OT-PSO searches for local optimum in the neighbor area of the global best solution by using the me...
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The challenges for building the component-based software architecture are how to estimate the assembly of reusable software components and make the properties forecast to the associated architecture. To address these ...
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software Process Workshop (SPW 2005) was held in Beijing on May 25-27, 2005. This paper introduces the motivation of organizing such a workshop, as well as its theme and paper gathering and review; and summarizes the ...
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software Process Workshop (SPW 2005) was held in Beijing on May 25-27, 2005. This paper introduces the motivation of organizing such a workshop, as well as its theme and paper gathering and review; and summarizes the main content and insights of 11 keynote speeches, 30 regular papers in five sessions of “Process Content”, “Process Tools and Metrics”, “Process Management”, “Process Representation and Analysis”, and “Experience Reports”, 8 software development support tools demonstration, and the ending panel “Where Are We Now? Where Should We Go Next?”.
For numeral recognition, when a single classifier cannot provide a decision which is 100 percent correct, multiple classifier should be able to achieve higher accuracy. This is because group decisions are generally be...
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computer vision and pattern recognition is hot research topic recently, fire and flame recognition is an important sub-topics. Although researchers proposed many methods, there exist high false alarm and complex envir...
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Recently, a new method that slow feature analysis (SFA), which can extract slowly varying feature of temporally varying signals, has been explored. SFA method is an extension of independent component analysis (ICA), w...
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ISBN:
(纸本)9781424463343
Recently, a new method that slow feature analysis (SFA), which can extract slowly varying feature of temporally varying signals, has been explored. SFA method is an extension of independent component analysis (ICA), which has been used to separate blind source signals. In this article, we present a simple and efficient SFA based method to separate blind signals according to their different smooth degree. The performance of the proposed mathod is higher than that of the conventional method ICA. Simulation illustrates the good performance of the proposed method.
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