Description: 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 matrices, irrespective of the size of images.
The numerals to be recognized are matched using nearest neighbor
classifier with each pattern and the best match pattern is considered as
the recognized numeral.The experimental results show accuracy of 96.2
for 500 images, representing the portion of trained data, with the system
being trained for 1000 images. The recognition result of 91 was
obtained for 250 test numerals other than the trained images. Further to
test the performance of the proposed scheme 4-fold cross validation has
been carried out yielding an accuracy of 89 -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 matrices, irrespective of the size of images.
The numerals to be recognized are matched using nearest neighbor
classifier with each pattern and the best match pattern is considered as
the recognized numeral.The experimental results show accuracy of 96.2
for 500 images, representing the portion of trained data, with the system
being trained for 1000 images. The recognition result of 91 was
obtained for 250 test numerals other than the trained images. Further to
test the performance of the proposed scheme 4-fold cross validation has
been carried out yielding an accuracy of 89 Platform: |
Size: 197632 |
Author:avi |
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Description: 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 Platform: |
Size: 2951168 |
Author:高进 |
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Description: 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 the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.-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 the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves. Platform: |
Size: 6144 |
Author:Gang Fu |
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Description: PCA用于交叉验证确定维数，很有用的程序，希望对你有用-PCA to determine the number of dimensions for cross-validation, very useful program, you want to be useful Platform: |
Size: 727040 |
Author:sun |
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Description: 神经网络训练，应用matlab7NN包，用一个隐藏层使用5折交叉验证。-Training the Neural Network
This script is something that I did for a course at Uni. It uses the Neural Networking package provided with MatLab 7 unfortunately I m not sure if it s available with the earlier versions of MatLab. This script uses the command lines for the package to perform the task, otherwise you can use the GUI that s provided, by typing nntool. This script shows 5 fold cross validation on a neural network with 1 hidden layer with a variable number of hidden nodes along with a single output. The entire process is done 2 times, because each time the data was encoded in a different manner, which in turn altered how much the Neural Network was able to learn from the data. Below you ll find the script to collect the data for the final results. Platform: |
Size: 2048 |
Author:kingking |
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Description: 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
数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维
数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发
动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测
精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition
time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em
bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af
fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap
plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida
tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to
the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an
aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em
bedding dimension selection. In compar ison w ith co nv Platform: |
Size: 342016 |
Author: |
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Description: 基于pls对光谱分析 包括数据读取，小波变换PCA分析，PLS建模，交叉验证-Pls include data on the spectrum based on reads, wavelet transform PCA analysis, PLS modeling, cross-validation Platform: |
Size: 4096 |
Author:liu |
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Description: 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. Platform: |
Size: 1024 |
Author:朱凡 |
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Description: 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 error rate and cross- validation error rates are equal, the larger the sample , this result is to be expected training error rate is generally lower than the test error rate , but when comparing the sample data came from the experiment there was an accident, that more samples group test error rate less than the training sample error rate is smaller In this experiment, the same set of data cross-validation error rate than independent test error rate, this anomaly is because the sample of reasons , cross-validation sample is small,And independent testing large number of training samples used , resulting in this situation.Classification online , fisher criterion is a straight line , while the Bayesian classifier is actually a closed curve similar to elliptical It is clear that the Bayesian classifier is better than the fisher classifier . Platform: |
Size: 8192 |
Author:崔杉 |
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Description: 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
noise estimation. In environments where data arrive sequentially, techniques
such as cross validation to achieve regularization or model selection
are not possible. The Bayesian approach, with extended Kalman filtering
at the parameter estimation level, allows for regularization within
a minimum variance framework. A multilayer perceptron is used to generate
the extended Kalman filter nonlinear measurements mapping. We
describe several algorithms at the noise estimation level that allow us to
implement on-line regularization.We also show the theoretical links between
adaptive noise estimation in extended Kalman filtering, multiple
adaptive learning rates, and multiple smoothing regularization coefficients. Platform: |
Size: 393216 |
Author:xiaochen |
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Description: 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 Platform: |
Size: 5120 |
Author:杨小超 |
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Description: 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.
Platform: |
Size: 1024 |
Author:罗婷丹 |
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Description: 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. Platform: |
Size: 5465088 |
Author:刘彦镔 |
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Description: 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. , Platform: |
Size: 5465088 |
Author:刘彦镔 |
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Description: 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 statistical model
using cross-validation.-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 statistical model
using cross-validation. Platform: |
Size: 1024 |
Author:Pranesh Krishnan |
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Search - cross validation