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数的分类
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作者 张希濂 《小学生课程辅导(数学辅导版)》 2004年第1期70-71,共2页
关键词 “数的分类” 香港 小学 基础知识 习题 解法
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AN IMPROVED ALGORITHM FOR SUPERVISED FUZZY C-MEANS CLUSTERING OF REMOTELY SENSED DATA 被引量:1
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作者 ZHANG Jingxiong Roger P Kirby 《Geo-Spatial Information Science》 2000年第1期39-44,共6页
This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de- creased as comparing with that by a conventional... This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de- creased as comparing with that by a conventional algorithm: that is, the classification accura- cy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzy c-means clustering, in particular when fuzziness is also accommodated in the assumed reference data. 展开更多
关键词 remotely sensed data (images) CLASSIFICATION fuzzyc-means clustering fuzzy membership values (FMVs) Mahalanobis distances covariance matrix
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RESEARCH ON SOME THEORETICAL PROBLEMS OF MAP DATA HANDLING USING FRACTAL APPROACH
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作者 WANG Qiao 《Geo-Spatial Information Science》 2000年第1期34-38,共5页
Some theoretical problems of fractal geographical map data handling are dis- cussed and some new methods about fractal dimension introducing, developing, comparing and estimating are proposed in this paper.
关键词 fractals fractal dimension SELF-SIMILARITY scale interval
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A retinal blood vessel extraction algorithm based on CART decision tree and improved AdaBoost
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作者 DIWU Peng-peng HU Ya-qi 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2019年第1期61-68,共8页
This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) t... This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm. 展开更多
关键词 classification and regression tree (CART) improved adptive boosting (AdaBoost) retinal blood vessel local binary pattern (LBP) texture
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Sure feature screening for high-dimensional dichotomous classification 被引量:2
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作者 SHAO Li YU Yuan ZHOU Yong 《Science China Mathematics》 SCIE CSCD 2016年第12期2527-2542,共16页
The curse of high-dimensionality has emerged in the statistical fields more and more frequently.Many techniques have been developed to address this challenge for classification problems. We propose a novel feature scr... The curse of high-dimensionality has emerged in the statistical fields more and more frequently.Many techniques have been developed to address this challenge for classification problems. We propose a novel feature screening procedure for dichotomous response data. This new method can be implemented as easily as t-test marginal screening approach, and the proposed procedure is free of any subexponential tail probability conditions and moment requirement and not restricted in a specific model structure. We prove that our method possesses the sure screening property and also illustrate the effect of screening by Monte Carlo simulation and apply it to a real data example. 展开更多
关键词 ultra-high dimensional data dichotomous classification sure screening property
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