數據挖掘軟件發展分析 第一代數據挖掘軟件 特點 支持一個或少數幾個數據挖掘算法 挖掘向量數據（vector-valued data） 數據一般一次性調進內存進行處理 典型的系統如Salford Systems公司早期的CART系統(www.salford-systems.com)
標簽： vector-valued data 數據挖掘 數據
上傳時間： 2014-01-14
上傳用戶：dragonhaixm
qrd_rls_AR_pred.m - use the QR decomposition-based RLS algorithm to predict complex-valued AR process.
標簽： decomposition-based qrd_rls_AR_pred complex-valued algorithm
上傳時間： 2015-12-27
上傳用戶：trepb001
rls_AR_pred.m - use basic RLS algorithm to predict real-valued AR proce
標簽： rls_AR_pred real-valued algorithm predict
上傳時間： 2015-12-27
上傳用戶：縹緲
Algo s in C++. A higly valued book on various algo s book in c++. Go through the book and u will be highly enriched. A fantastic book for beginners.
標簽： book through various valued
上傳時間： 2017-05-01
上傳用戶：fanboynet
- 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
Apply the standard QR-decomposition based LSL algorithm using angle-normalized error to predict/estimate complex-valued processes.
標簽： QR-decomposition angle-normalized algorithm standard
上傳時間： 2015-12-27
上傳用戶：cc1
this directory contains the following: * The acdc algorithm for finding the approximate general (non-orthogonal) joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices. [acdc.m] * The acdc algorithm for finding the same for a set of Symmetric matrices. [acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version [acdc.m], rather than the Symmetric version[acdc_sym] is preferable. * A function that finds an initial guess for acdc by applying hard-whitening followed by Cardoso s orthogonal joint diagonalizer. Note that acdc may also be called without an initial guess, in which case the initial guess is set by default to the identity matrix. The m-file includes the joint_diag function (by Cardoso) for performing the orthogonal part. [init4acdc.m]
標簽： approximate directory algorithm the
上傳時間： 2014-01-17
上傳用戶：hanli8870
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
The frequency domain plays an important role in image processing to smooth, enhance, and detect edges of images. Although image data typically does not include imaginary values, the fast Fourier transform (FFT) has been used for obtaining spectra. In this paper, the fast Hartley transform (FHT) is used to transform two-dimensional image data. Because the Hartley transform is real valued, it does not require complex operations. Both spectra and autocorrelations of two-dimensional ultrasound images of normal and abnormal livers were computed.
標簽： processing frequency important enhance
上傳時間： 2014-01-08
上傳用戶：1051290259
