- 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.
標簽：
Environments
multiplexer
Continuous
XCS
上傳時間：
2015-07-04
上傳用戶：Avoid98
OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images.
標簽：
OTSU
segmentation
Gray-level
segmented
上傳時間：
2017-04-24
上傳用戶：yuzsu
