搜索资源列表
waili
- waili小波算法包,在windows下编译通过的lib-Uses integer wavelet transforms based on the lifting Scheme ¢ Provides various wavelet transforms of the Cohen-Daubechies-Feauveau family of biorthogonal wavelets ¢ Provides crop and merge operations on wavelet-
111
- LIBSVM是台湾大学林智仁(Lin Chih-Jen)副教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包,他不但提供了编译好的可在Windows系列系统的执行文件,还提供了源代码,方便改进、修改以及在其它操作系统上应用;该软件还有一个特点,就是对SVM所涉及的参数调节相对比较少,提供了很多的默认参数,利用这些默认参数就可以解决很多问题;并且提供了交互检验(Cross Validation)的功能。
libsvm_src_2.6NOTE
- LIBSVM是台湾大学林智仁(Lin Chih-Jen)副教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包,他不但提供了编译好的可在Windows系列系统的执行文件,还提供了源代码,方便改进、修改以及在其它操作系统上应用;该软件还有一个特点,就是对SVM所涉及的参数调节相对比较少,提供了很多的默认参数,利用这些默认参数就可以解决很多问题;并且提供了交互检验(Cross Validation)的功能。该软件包可以在http://www.csie.ntu.edu.tw/~c
P411
- 模式识别作业第411面的源程序,实现交叉验证算法-Pattern recognition operations side of the source 411 to achieve cross-validation algorithm
libsvm-mat-2[1].9-11
- LIBSVM是台湾大学林智仁(Lin Chih-Jen)副教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包,他不但提供了编译好的可在Windows系列系统的执行文件,还提供了源代码,方便改进、修改以及在其它操作系统上应用;该软件还有一个特点,就是对SVM所涉及的参数调节相对比较少,提供了很多的默认参数,利用这些默认参数就可以解决很多问题;并且提供了交互检验(Cross Validation)的功能。该软件包可以在http://www.csie.ntu.edu.tw/~c
refpaper6_hcrnumkannada
- Abstract. This paper describes a system for isolated Kannada handwritten numerals recognition using image fusion method. Several digital images corresponding to each handwritten numeral are fused to generate patterns, which are stored in 8x8 ma
PLS
- M-files for PLS, PLS-DA, with leave-one-out cross-validation and prediction
Density_Estimation
- 分别采用GMM和KDE对Iris数据集进行密度建模,并进行对比。通过EM算法来确定GMM参数,通过交叉验证来确定K值-GMM and KDE respectively Iris data set of density modeling, and compared. GMM by EM algorithm to determine the parameters of K determined by the value of cross-validation
FeatureSelection_MachineLearning
- Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for
bp
- 一个matlab写的bpANN程序,参数优化采用交叉验证办法.-Write a matlab bpANN process parameter optimization using cross-validation approach.
CV_split_data
- 交叉验证源程序 评价模型性能的一种方式-cross validation
Adaptive-Embedding-Dimension
- 嵌入维数自适应最小二乘支持向量机 状态时间序列预测方法 Condition Time Series Prediction Using Least Squares Support Vector Machine with Adaptive Embedding Dimension 针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应 最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参
BPcrossvalind
- MATLAB的BP交叉验证的程序,自己编写的,可直接运行,供大家参考。-MATLAB-BP cross-validation procedure, I have written can be directly run, for your reference.
Comparison-of-Bayesian-and-fisher
- 训练错误率和交叉验证错误率相等,在样本比较大时,这个结果是可以预期的;训练错误率一般低于测试错误率,但是当样本数据比较少时,实验也出现了意外,样本多的那组测试错误率比样本少的训练错误率还要小;在本实验中,同组数据的交叉验证错误率比独立测试错误率高,这个反常现象是因为样本的原因所致,交叉验证的样本小,而独立测试时所用训练样本数目大,因而出现这种情况。分类线上,fisher准则是一条直线,而贝叶斯分类器实际上是一个类似椭圆的封闭曲线;很明显,贝叶斯分类器比fisher分类器要好。-Training
Adaptive-Online-Learning
- 基于EKF的神经网络自适应在线学习算法,包含例子和文档。-We show that a hierarchical Bayesian modeling approach allows us to perform regularization in sequential learning. We identify three inference levels within this hierarchy: model selection, parameter estimation, and
chapter8
- chapter8_1.m为使用交叉验证的GRNN神经网络预测程序 chapter8_2.m为BP和GRNN效果比较程序-chapter8_1.m for the GRNN neural network prediction program using cross-validation chapter8_2.m for BP and GRNN effect of the program
locv
- 最先进的KPCA主成分提取法,加最先进的高斯SVM法,再加传统的交叉验证学习预测法。-The most advanced KPCA principal components extraction method, and the most advanced gaussian SVM method, then add the traditional cross validation forecast method of learning.
MPI
- MPI实现交叉证验的实验代码,附有测试文件,运行在LInux系统,需要配置MPI环境。-MPI implementation of cross-validation of experimental code, accompanied by the test file, run LInux system, you need to configure the MPI environment.
crossMatch
- 交叉证验的多线程实现版本,附有代码的测试文件。需要在Linux下运行。,-Cross-validation of the multi-threaded implementation version of the test code is attached to the file. Need to run under Linux. ,
read
- CVPARTITION Create a cross-validation partition for data. An object of the CVPARTITION class defines a random partition on a set of data of a specified size. This partition can be used to define test and training sets for validating a statis