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| - | ====== | + | ====== |
| - | RVKDE, standing for Relaxed Variable Kernel Density Estimation, is an | + | Here is the start page of the wiki of [[http:// |
| - | integrated software of classification, | + | |
| - | document explains | + | |
| - | RVKDE is available at ' | + | If you want to join us, e-mail to [[darby@ee.ncku.edu.tw|Tien-Hao Chang]] for a privileged account and you can start to edit this wiki. |
| - | COPYRIGHT file before using RVKDE. | + | |
| - | ====== | + | ====== |
| - | - Quick start | + | All releases |
| - | - Installation and data format | + | |
| - | - // | + | |
| - | - // | + | |
| - | - Additional tools | + | |
| - | - Additional information | + | |
| - | + | ||
| - | ====== Quick start ====== | + | |
| - | If you are new to SVM and if the data is not large, please go to //tool// | + | |
| - | directory and use // | + | |
| - | data scaling to parameter selection, automatic. | + | |
| - | + | ||
| - | Usage: kde-easy.pl TRAINING_FILE [TESTING_FILE] | + | |
| - | + | ||
| - | More information about parameter selection can be found in // | + | |
| - | + | ||
| - | ====== Installation and data format ====== | + | |
| - | **On Linux systems** | + | |
| - | - Donwload the latest version from the official site of RVKDE.< | + | |
| - | wget http:// | + | |
| - | </ | + | |
| - | - Extract the archive.< | + | |
| - | tar zxvf rvkde-current-linux32.tgz | + | |
| - | </ | + | |
| - | - Execute them without arguments to show the usages.< | + | |
| - | kde-train.pl | + | |
| - | kde-predict.pl | + | |
| - | </ | + | |
| - | + | ||
| - | -------------------------------------------------------------------------------- | + | |
| - | **On Windows systems** | + | |
| - | + | ||
| - | Before installing RVKDE, please make sure that Perl is installed. | + | |
| - | [[http:// | + | |
| - | Windows. | + | |
| - | - Donwload the latest version from the official site of RVKDE.< | + | |
| - | wget http:// | + | |
| - | </ | + | |
| - | - Extract the archive using a compressor (e.g. 7-zip, WinRAR or WinZip). | + | |
| - | - Invoke the Windows console.< | + | |
| - | [Start] -> [Run] -> Type ' | + | |
| - | </ | + | |
| - | - Execute them without arguments to show the usages. | + | |
| - | - Using //cd// to the directory of your extracted RVKDE. | + | |
| - | - Execute them without arguments to show the usages.< | + | |
| - | kde-train.pl | + | |
| - | kde-predict.pl | + | |
| - | </ | + | |
| - | + | ||
| - | The format of training and testing data file is: | + | |
| - | + | ||
| - | < | + | |
| - | < | + | |
| - | . | + | |
| - | . | + | |
| - | . | + | |
| - | </ | + | |
| - | + | ||
| - | Each line contains an instance and is ended by a ' | + | |
| - | classification, | + | |
| - | is supported). For regression, < | + | |
| - | real number. < | + | |
| - | integer starting from 1 and < | + | |
| - | file are only used to calculate accuracy or errors. If they are unknown, just | + | |
| - | fill the first column with any numbers. | + | |
| - | + | ||
| - | A sample classification data included in this package is **heart.scale**. | + | |
| - | Type // | + | |
| - | data and output the model file // | + | |
| - | called // | + | |
| - | heart.scale.model output// to see the prediction accuracy. The //output// file | + | |
| - | contains the predicted class labels.< | + | |
| - | kde-train.pl heart.scale # train | + | |
| - | kde-predict.pl heart.scale.t heart.scale.model output # predict | + | |
| - | vi output # predicted results | + | |
| - | </ | + | |
| - | + | ||
| - | ====== kde-train.pl usage ====== | + | |
| - | < | + | |
| - | Usage: kde-train.pl [OPTION]... TRAINING_FILE [MODEL_FILE] | + | |
| - | kde-train.pl is a Perl wrapper for RVKDE to make the using of RVKDE like | + | |
| - | LIBSVM. Make sure the rvkde binary executable file is in the same directory | + | |
| - | of this script. | + | |
| - | + | ||
| - | Options: | + | |
| - | -a alpha: set alpha in kernel function (default: 1) | + | |
| - | -b beta: set beta in kernel function (default: 1) | + | |
| - | -h print this help, then exit | + | |
| - | -s ks: set maximum ks in kernel function (default: 10) | + | |
| - | -t kt: set kt in kernel function (default: 10) | + | |
| - | -v n: n-fold cross validation mode | + | |
| - | + | ||
| - | Examples: | + | |
| - | kde-train.pl heart.scale # generate heart.scale.model | + | |
| - | kde-train.pl heart.scale heart.scale.kde-model # different model name | + | |
| - | kde-train.pl -v 5 heart.scale # five-fold cross validation | + | |
| - | kde-train.pl -s 30 heart.scale # using different parameters | + | |
| - | + | ||
| - | Option -v splits the data into n parts by modulo arithmetic and calculates | + | |
| - | cross validation performance on them. | + | |
| - | + | ||
| - | Report bugs to < | + | |
| - | </ | + | |
| - | + | ||
| - | ====== kde-predict.pl usage ====== | + | |
| - | < | + | |
| - | Usage: kde-predict.pl [OPTION]... TESTING_FILE MODEL_FILE OUTPUT_FILE | + | |
| - | kde-predict.pl is a Perl wrapper for RVKDE to make the using of RVKDE like | + | |
| - | LIBSVM. Make sure the rvkde binary executable file is in the same directory | + | |
| - | of this script. | + | |
| - | + | ||
| - | Options: | + | |
| - | -a alpha: set alpha in kernel function (default: 1) | + | |
| - | -b beta: set beta in kernel function (default: 1) | + | |
| - | -h print this help, then exit | + | |
| - | -s ks: set ks in kernel function (default: 10) | + | |
| - | -t kt: set kt in kernel function (default: 10) | + | |
| - | + | ||
| - | TESTING_FILE is the testing data you want to predict. | + | |
| - | MODEL_FILE is the model file generated by kde-train.pl. | + | |
| - | OUTPUT_FILE contains the predicted results by RVKDE. | + | |
| - | + | ||
| - | Examples: | + | |
| - | kde-predict.pl heart.scale.t heart.scale.model output | + | |
| - | kde-predict.pl -s 30 -t 30 heart.scale.t heart.scale.model output | + | |
| - | + | ||
| - | Unlink LIBSVM, RVKDE is a lazy-learning tool. The model file contains only | + | |
| - | basic information and the real prediction model is constructed right before the | + | |
| - | prediction operation. It means you can specify different parameters (alpha, | + | |
| - | beta, ks and kt) for kde-predict.pl as those specified for kde-train.pl. Only | + | |
| - | one condition must be hold: the ks for kde-predict.pl is less than or equal to | + | |
| - | the ks for kde-train.pl. | + | |
| - | + | ||
| - | Report bugs to < | + | |
| - | </ | + | |
| - | + | ||
| - | ====== Additional tools ====== | + | |
| - | See the README file in the tool directory. | + | |
| - | + | ||
| - | ====== Additional information ====== | + | |
| - | If you find RVKDE helpful, please cite it as | + | |
| - | + | ||
| - | Yen-Jen Oyang, Shien-Ching Hwang, Yu-Yen Ou, Chien-Yu Chen, and Zhi-Wei Chen. | + | |
| - | Data Classification with the Radial Basis Function Network Based on a Novel | + | |
| - | Kernel Density Estimation Algorithm. //IEEE Transactions on Neural Networks//, | + | |
| - | **16**, 225-236, 2005. | + | |
| - | + | ||
| - | Software available at ' | + | |
| - | + | ||
| - | For any questions and comments, please email darby@ee.ncku.edu.tw | + | |
| - | + | ||
| - | Acknowledgments: | + | |
| - | This work was supported in part by the National Science Council of Taiwan via | + | |
| - | the grant NSC 92-2213-E-002-095. The authors thank their group members and | + | |
| - | users for many helpful discussions and comments. | + | |
| + | ====== How to start? ====== | ||
| + | - [[rvkde: | ||
| + | - [[rvkde: | ||