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5个遗传算法源码
- （其中已经包含5个源码）所附源程序C或C++代码文件及其可执行文件 Scs.cpp 基本分类算法源程序， 输入数据文件cfile.txt,efile.txt,gfile.txt,pfile.txt ,rfile.txt,tfile.txt Sga.c 基本遗传算法源程序, 输入数据文件input，输出文件output A_life.c 基于遗传算法的人工生命模拟源程序, 输入数据文件world GA_nn.c 基于遗传算法优化神经网络结构源程序,输入数据文件sample Patmat.c 基于遗
FASBIR
- Descr iption: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble. Reference:
C45Rule-PANE
- Descr iption: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagn
Mesdaghi - Pathing Tutorial
- PathPlannerApp Manual With Tutorial.doc is a path planning-tutorial that provides detailed explanation and pseudo-codes. In order to get the most out of the tutorial, you should start with the \"PathPlannerApp.base\" and avoid looking through th
IncrementalRandomNeurons
- 本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hid
svm_v0.55beta
- 最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, \"The Nature of Statistical Learning Theory\", Springer-Verl
DGPSO.rar
- 用于求解约束优化问题的算法，算法为差分进化/遗传算法/微粒群算法的融合。对于“[7] T. P. Runarsson and X. Yao, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 284-294, Sep. 2000”中给出的13个标准测试函数，均能得到问题最优解。如有任何疑问，请于http://2shi.phphube
PP_Algorithm
- 将PSO和LBG结合在一步迭代过程中，并使用particle-pair（PP）搜索问题空间的算法-LBG will be in conjunction with PSO and step iterative process, and use the particle-pair (PP) problem space search algorithm
NeuralNetworks
- Neural Networks at your Fingertips.rar =============== Network: Adaline Network =============== Application: Pattern Recognition Classification of Digits 0-9 Author: Karsten Kutza Date: 15.4.96 Reference: B. Widrow, M
ClusterEnsembleV10
- Alexander Strehl的CLUSTER ENSEMBLE算法----------------------------------------------------------------------- CLUSTERENSEMBLE README Alexander Strehl Version 1.0 2002-04-20 -------------------------------------------------------------------
x
- This derivation of the normalised least mean square algorithm is based on Farhang- Boroujeny 1999, pp.172-175, and Diniz 1997, pp 150-3. To derive the NLMS algorithm we consider the standard LMS recursion, for which we select a variable step size
PP
- The paper gives Survey of robots used in industries and other robots in labs.
BPtimeserierforcasting
- 一个关于bp时间序列预测的pp 希望对大家有用-A time series prediction on bp hope useful ppt
feature_selection_similarity
- Feature selection method using similarity measure and fuzzy entroropy measures based on the article: P. Luukka, (2011) Feature Selection Using Fuzzy Entropy Measures with Similarity Classifier, Expert Systems with Applications, 38, pp.
article-of-pp
- 三篇投影寻踪的经典文献 基于投影寻踪和遗传算法的一种非线性系统建模方法； 基于投影寻踪模型和加速遗传算法的石羊河流域水资源承载力综合评价； 基于遗传算法的投影寻踪聚类模型对银川市各地区水资源承载能力评估. -Three classic literature Projection Pursuit based on genetic algorithm based on projection pursuit model and accelerating genetic algorith
matlab-for-pp-valuable
- 投影寻踪源程序代码，想学习pp算法的会有帮助-The Projection Pursuit source code will help learning pp algorithm
ANFIS
- C codes and simulation examples for the ANFIS by Jang (Absolutely original version )-This attached file contains C code and simulation examples for the ANFIS (Adaptive-Network-based Fuzzy Inference System) architecture proposed in [1,2]. Related
OPAUC
- One-Pass AUC 优化的matlab代码。参考文献：Wei Gao, Rong Jin, Shenghou Zhu and Zhi-Hua Zhou. One-Pass AUC Optimzation. In: Proceedings of the 30th International conference on Machine Learning (ICML 13), Atlanta, GA, 2013, JMLR: W&CP 28(3), pp.906-914. -This pa