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Description: C-C方法及改进的C-C方法重构相空间的matlab程序
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性能：
3000数据耗时3分钟
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参考文献：
1、Nonlinear dynamics, delay times, and embedding windows.pdf
2、基于改进的C-C方法的相空间重构参数选择4.pdf
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文件夹说明：
1、C_C_Method_luzhenbo2.m - 程序主文件，直接运行此文件即可!
2、LorenzData.dll - 产生Lorenz离散数据
3、DuffingData.dll - 产生Duffing离散数据
4、RosslerData.dll - 产生Rossler离散数据
5、ccFunction.dll - 计算S(m,N,r,t) - 原C-C方法中计算S(m,N,r,t)，改进的C-C方法中计算S2(m,N,r,t)
6、ccFunction_luzhenbo.dll - 计算S(m,N,r,t) - 改进的C-C方法中计算S1(m,N,r,t)
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致谢：
此稿本次修改的部分灵感来源于与研学论坛网友“张文鸽”和“yangfanboy”的讨论，在此表示感谢!-C-C methodology and improvement of C-C method reconstruction phase space of Matlab procedures-------------------------------- performance : 3000 data time-consuming three minutes------------------------------- References : 1, Nonlinear dynamics, delay times, and embedding windows.pdf 2, Based on the C-C method of phase space reconstruction parameters choice 4.pdf-------------------------------- folders : one, C_C_Method_luzhenbo2.m-main proceedings, direct running this file can be! 2. LorenzData.dll- Lorenz have three discrete data, DuffingData.dll- Duffing have four discrete data, RosslerData.dll- Rossler have discrete data 5, ccFunction.dll-calculated S (m, N, r, t)- the original C-C method to calculate S (m, N, r, t), to improve the C-C method of calculating S2 (m, N, r, t) 6,
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Size: 475136 |
Author: jiao |
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Description: 这个程序实现了Francis R. Bach的Bolasso算法，用于特征选取和预测。主要用于高纬度问题的特征选取，它使用了带有Bootstrap方法的自助抽样的正则化回归，并使用了Karl Skoglund的lars实现。-This procedure achieved Francis R. Bach s Bolasso algorithms for feature selection and forecasting. The main problem for high-latitude feature selection, it uses a method of self-help Bootstrap sampling Tikhonov reunification, and Karl Skoglund used to achieve the lars.
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Size: 198656 |
Author: xuechaoling |
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Description: R-Tree空间索引算法的研究历程和最新进展，挺详细的-R-Tree spatial index algorithm and the latest progress of the course, very detailed
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Size: 46080 |
Author: lyd |
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Description: r tree generation in matlab is given
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Size: 5120 |
Author: dharmistha |
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Description: matlab数据结构 算法工具箱, 包括堆栈 队列 2叉树 红黑树 -Data Structures & Algorithms Toolbox
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The Data Structures & Algorithms Toolbox provides advanced data
structures and algorithms for the Matlab(r) 5 environment. It implements
functions to create and destroy singly and doubly linked list, stack,
queue, binary tree and red-black (balanced) binary tree. Data elements
can be inserted, deleted or searched for. A graphical representation of
trees can be shown. All algorithms are clearly written as m-files, which
allows the toolbox to be used as a teaching aid in educational data
structures courses. This is the first implementation available of
advanced datatypes, common in other languages, for Matlab.
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Size: 116736 |
Author: austin |
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Description: Applied Statistics Using SPSS,STATISTICA,MATLAB and R
Joaquim P.
ISBN 978-3-540-71971-7 Springer Berlin Heidelberg New York-Applied Statistics Using SPSS, STATISTICA,MATLAB and R
Inclusion of R as an application tool. As a matter of fact, R is a free
software product which has nowadays reached a high level of maturity
and is being increasingly used by many people as a statistical analysis
tool.
Chapter 3 has an added section on bootstrap estimation methods, which
have gained a large popularity in practical applications.
A revised explanation and treatment of tree classifiers in Chapter 6 with
the inclusion of the QUEST approach.
Several improvements of Chapter 7 (regression), namely: details
concerning the meaning and computation of multiple and partial
correlation coefficients, with examples a more thorough treatment and
exemplification of the ridge regression topic more attention dedicated to
model evaluation.
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Size: 6702080 |
Author: ABC |
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Description: 本书介绍了如何使用 SPSS、MATLAB、表列法和 R 进行数据描述、统计推断、分类和回归、因子分析、生存数据和方向统计。第二版包括 R 语言、关于引导估计方法的新章节和对树分类器的改进处理, 以及其他示例和练习。(this book shows how to use SPSS, MATLAB, STATISTICA and R for data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. The 2nd edition includes the R language, a new section on bootstrap estimation methods and an improved treatment of tree classifiers, plus additional examples and exercises.)
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Size: 6675456 |
Author: lorry0917 |
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