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ESA is an unsupervised approach to word segmentation previously proposed by Wang, which is an iterative process consisting of three phases: Evaluation, Selection and Adjustment. In this article, we propose ExESA, the extension of ESA. In ExESA, the original approach is extended to a 2-pass process and the ratio of different word lengths is introduced as the third type of information combined with...
In this paper, we focus on student study quality evaluation in the educational system. To achieve a better performance on the teaching thinking of the students and improve teaching efficiency and learning quality, we proposed a new method based on the cloud model. The results of the experiments show that the new method is effective and operable. The application of the cloud model in the personalized...
This article proposes two modifications of a new unsupervised method of word segmentation consisting of three phases: Evaluation, Selection, and Adjustment (ESA), which was presented in our early paper. Lowest Relative Value (LRV) is the core algorithm in ESA The whole method has only one parameter (the exponent in LRV) that can be approximately predicted by the empirical formulae. In this article,...
Which factors influence life cycle cost of torpedo heavily and which factors influence it softly should be analyzed before the life cycle cost model of torpedo is set up. That is to say, the sensitive parameters of life cycle cost should be identified. Using grey relational entropy of the grey system theory identifies the sensitive parameters of torpedo life cycle cost can classify the influencing...
The application of association rule mining to classification has led to a new family of classifiers which are often referred to as associative classifiers (ACs). An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable...
With more than twenty years development, rough set theory has been successfully applied in the fields of expert systems, machine learning, and knowledge discovery in databases. Attribute reduction is an important research issue in rough set theory. At present, there are many different attribute reduction definitions, for example, attribute reduction based on Pawlak, based on information entropy and...
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