文件列表:

A Java implementation of SMO for training SVM\smo-java\tic-tac-toe (54938, 2001-06-06)
A Java implementation of SMO for training SVM\smo-java\tic-tac-toe.binary (101548, 2004-12-04)
A Java implementation of SMO for training SVM\smo-java\heart_scale (27670, 2004-11-24)
A Java implementation of SMO for training SVM\smo-java\GetOpt.java.bak (13360, 2005-10-15)
A Java implementation of SMO for training SVM\smo-java\GetOpt.java (13371, 2005-10-15)
A Java implementation of SMO for training SVM\smo-java\Smo.java.bak (31229, 2005-10-16)
A Java implementation of SMO for training SVM\smo-java\Smo.java (31238, 2005-10-16)
A Java implementation of SMO for training SVM\smo-java (0, 2004-12-07)
A Java implementation of SMO for training SVM (0, 2005-10-14)

Java implementation of SMO (Sequential Minimal Optimization) for SVM Developed by Xiaoqian Jiang (xiaoqian-jiang@uiowa.edu) Hwanjo Yu (hwanjoyu@cs.uiowa.edu) Using This Code: This code is publicly available to facilitate research in the related areas of data mining and machine learning. If you publish material based on this code, please refer to the source as follows, to help others to obtain the same code and reproduce your experiments. SVM-JAVA: A Java implementation of the SMO (Sequential Minimal Optimization) for training SVM. Computer Science Department, University of Iowa. http://hwanjoyu.org/svm-java, 2005 Bibtex entry: @MISC{UIOWA05svm-java, title = "{SVM-JAVA}: A Java Implementation of the SMO (Sequential Minimal Optimization) for training SVM", year = "2005", howpublished = "Computer Science Department, University of Iowa. http://hwanjoyu.org/svm-java" } Contact xiaoqian-jiang@uiowa.edu for questions 1. Files GetOpt.java: The command argument parser for Java Smo.java: The main source for the SMO program tic-tac-toe: Sample data file for dense format heart_scale: Sample data file for sparse format tic-tac-toe.binary: Sample data file for binary format 2. Tested platform Linux, Windows 3. How to compile % javac Smo.java % javac GetOpt.java 4. How to run Train: % java Smo -f [datafile] -d [column NO.] -Other parameters e.g., java Smo -f heart_scale -s e.g., java Smo -f tic-tac-toe -d 27 e.g., java Smo -f tic-tac-toe.binary -b Test: % java Smo -f [datafile] -m [model file] -a e.g., java Smo -f tic-tac-toe -m java-svm.model -a 5. Parameters java Smo -h usage: -h -f data_file_name (default: java-svm.data) -m svm_file_name (default: java-svm.model) -o output_file_name (default: java-svm.output) -n N -d d -c C (default: 0.05) -t tolerance (default: 0.001) -e epsilon (default: 0.001) -p two_sigma_squared (default: 2) -l (is_linear_kernel) (default: RBF kernel) -s (is_sparse_data) (default: false) -b (is_binary) (default: false) -a (is_test_only) (default: false) 5. Data format The data file is a flat text file, and each data point occupies one line. For dense format, attribute values are followed by the class label (+1 or -1). (See "tic-tac-toe") Sparse format is the same as that in libsvm or svm light. (See "heard_scale") For binary format, feature values are binary, so only the indexes are specified. (See "tic-tac-toe.binary") 6. Output java-svm.model: The SVM model file generated after training java-svm.output: Function outputs on the training data 7. Saving and loading model parameters The output order of the model paramters will be 1. The number of attributes d. 2. The flag is-sparse-data 3. The flag is-binary 4. The flag is-linear-kernel 5. The threshold b 6. If the linear kernel is used: (a) The weight vector w 7. If non-linear kernel is used (a) Kernel paramters (b) The number of support vectors (c) The Lagrange multipliers of the support vectors (d) The support vectors, one per line

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