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Description: - XCS for Dynamic Environments
+ Continuous versions of XCS
+ Test problem: real multiplexer
+ Experiments: XCS is explored in dynamic environments with different magnitudes of change to the underlying concepts.
+Reference papers:
H.H. Dam, H.A. Abbass, C.J. Lokan, Evolutionary Online Data Mining – an Investigation in a Dynamic Environment. 2005, accepted for a book chapter in Springer Series on Studies in Computational Intelligence
H.H. Dam, H.A. Abbass, C.J. Lokan, Be Real! XCS with Continuous-Valued Inputs. IWLCS 2005, (International Workshop on Learning Classifier Systems). Washington DC, June 2005.-- XCS for Continuous Dynamic Environments Test versions of XCS problem : real multiplexer Experiments : XCS is explored in dynamic environments with di fferent magnitudes of change to the underlying concepts. Reference papers : H. H. Dam, H. A. Abbass, C. J. Lokan. Evolutionary Online Data Mining-an Investiga tion in a Dynamic Environment. 2005. accepted for a book chapter in Springer Series o n Studies in Computational Intelligence H.H. D am, H. A. Abbass, C. J. Lokan. Be Real! XCS with Continuous - Valued Inputs. IW LCS 2005. (International Workshop on Learning Classifi er Systems). Washington DC, June 2005.
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Size: 23001 |
Author: 李恆寬 |
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Description: - XCS for Dynamic Environments
+ Continuous versions of XCS
+ Test problem: real multiplexer
+ Experiments: XCS is explored in dynamic environments with different magnitudes of change to the underlying concepts.
+Reference papers:
H.H. Dam, H.A. Abbass, C.J. Lokan, Evolutionary Online Data Mining – an Investigation in a Dynamic Environment. 2005, accepted for a book chapter in Springer Series on Studies in Computational Intelligence
H.H. Dam, H.A. Abbass, C.J. Lokan, Be Real! XCS with Continuous-Valued Inputs. IWLCS 2005, (International Workshop on Learning Classifier Systems). Washington DC, June 2005.-- XCS for Continuous Dynamic Environments Test versions of XCS problem : real multiplexer Experiments : XCS is explored in dynamic environments with di fferent magnitudes of change to the underlying concepts. Reference papers : H. H. Dam, H. A. Abbass, C. J. Lokan. Evolutionary Online Data Mining-an Investiga tion in a Dynamic Environment. 2005. accepted for a book chapter in Springer Series o n Studies in Computational Intelligence H.H. D am, H. A. Abbass, C. J. Lokan. Be Real! XCS with Continuous- Valued Inputs. IW LCS 2005. (International Workshop on Learning Classifi er Systems). Washington DC, June 2005.
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Size: 22528 |
Author: 李恆寬 |
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Description: 分类器的设计，包括knn方法和BP的方法。对一系列样本点进行分类-Classifier design, including methods and BP KNN method. On a series of sample points to classify
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Size: 2032640 |
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Description: Duda.R.O, Hart.P.E, Stork.D.G, Pattern Classification 2nd Ed by Wiley.C
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Size: 14236672 |
Author: Dinesh |
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Description: K最邻近分类器设计的MATLAB代码，有代码解释-K nearest neighbor classifier design in MATLAB code
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Size: 3072 |
Author: lilei |
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Description: neural network basic book
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Size: 6572032 |
Author: Deepak |
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Description: 基于神经网络的遥感图像分类取得了较好的效果,但存在固有的过学习、易陷入局部极小等缺点.支持向量机机器学习方法,根据结构风险最小化(SRM)原理,表现出很多优于其他传统方法的性能,本研究的基于多类支持向量机分类器的遥感图像分类取得了达95.4 的分类精度.但由于遥感图像分类类别多,所需训练样本较大,人工选择效率较低,为此提出以人工选择初始聚类质心、C均值模糊聚类算法自动标注训练样本的基于多类支持向量机的半监督式遥感图像分类方法,期望能在获得适用的分类精度的基础上有效提高分类效率-Neural net based remote sensing image classification has obtained good results. But neural net has inherent
flaws such as overfitting and local minimums. Support vector machine (SVM), which is based on Structural Risk Min-
imization(SRM), has shown much better performance than most other existing machine learning methods. Using mul-
ti-class SVM classifier high class rate of 95.4 is obtained. But for the class number of remote sensing image is much
great, manually obtaining of training samples is a much time-consuming work. So a multi-class SVM based semi-super-
vised approach is presented. It is choosed that the initial clustering centroids manually first, then label the samples as
the training ones automatically with fuzzy clustering algorithm. It is believed that this method will upgrade the classifi-
cation efficiency greatly with practicable class rate
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Size: 25600 |
Author: cissy |
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Description: Supervised Machine Learning--A Review of Classification Techniques.rar
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Size: 348160 |
Author: messer |
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Description: Pixel classification code using K-Nearest Neighbor Classifier.Here three pixel values of color image are taken as training sample.Color based segmented image was used to classify the object into three classes.-Pixel classification code using K-Nearest Neighbor Classifier.Here three pixel values of color image are taken as training sample.Color based segmented image was used to classify the object into three classes.
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Size: 1024 |
Author: poo |
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Description: probability, unsupervised classifi.
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Size: 47104 |
Author: 他里雾 |
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