BSVM:*****************************************************************           COPYRIGHT NOTIFICATIONBSVM can be freely used for research purpose.Use for commercial purposes is expressly prohibitedwithout contacting the authors.BSVM is provided "as is" without express or implied warranty.Chih-Wei Hsu and Chih-Jen LinDepartment of Computer Science andInformation EngineeringNational Taiwan University Taipei 106, Taiwane-mail: cjlin@csie.ntu.edu.tw*****************************************************************           INSTRUCTIONS          1. Create the bsvm directory structure with        unzip bsvm.zip   This produces the directory bsvm and several subdirectories.2. On Unix systems, type `make' to build the `bsvm-train' and `bsvm-predict'   programs. On other systems, consult `Makefile' to build them or use the   pre-built binaries (Windows binaries are in the subdirectory `Windows').3. Find options of bsvm		bsvm-train 4. Run the sample problems by executing bsvm. A model    file is generated	bsvm-train -t 2 -c 1000  vehicle.scale vehicle_model5. Test the classifier	bsvm-predict vehicle.scale vehicle_model classified_result    test data: vehicle.scale (a 4-class classification problem)   support vectors: vehicle_model   classified_result: results after classification *****************************************************************           FILE FORMATS and PARAMETERSThe format of training and testing data file is:<label> <index1>:<value1> <index2>:<value2> ......<label> is the target value of the training data. For classification,it should be an integer which identifies a class (multi-class classificationis supported). For regression, it's any real number. <index> is an integer starting from 1, <value> isa real number. The labels in the testing data file are only used tocalculate accuracy. If they are unknown, just fill this column with a number.The new release of BSVM borrows the structure of libsvm(http://www.csie.ntu.edu.tw/~cjlin/libsvm). Similar options are also adopted.Type bsvm-train to know the usage of bsvm:For more explanation of these options, please check the README of libsvm.The main difference from libsvm is -s and -q.  -s 0: bound-constrained multi-class support vector classification (SVC)  -s 1: multi-class SVC from solving a bound-constrained problem  -s 2: multi-class SVC from Crammer and Singer  -s 3: bound-constrained support vector regressionThe option -q gives the size of sub-problems for -s 0, 1, and 3.The size of sub-problems using -s 2 is always the number of classes.Note that -v now stands for cross validataion. In BSVM 1.1, -v isthe verbosity.*****************************************************************           ADDITIONAL INFORMATIONChih-Wei Hsu and Chih-Jen Lin1. A simple decomposition method for support vector machineshttp://www.csie.ntu.edu.tw/~cjlin/papers/decomp.ps.gz2. A comparison on methods for multi-class support vector machines.http://www.csie.ntu.edu.tw/~cjlin/papers/multisvm.ps.gz3. Decomposition Methods for Linear Support Vector Machineshttp://www.csie.ntu.edu.tw/~cjlin/papers/linear.pdfAcknowledgments:The authors thank Chih-Chung Changfor many helpful disussions and comments.Part of the software implementationalso benifited from his help.BSVM uses the opmization solver TRONby Chih-Jen Lin and Jorge More'.Please read the COPYRIGHT NOTIFICATIONbefore using it.The TRON homepage is in http://www.mcs.anl.gov/~more/tron