How to Run WEKA Demo SVM in WEKA

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How to Run WEKA Demo SVM in WEKA T. B. Chen 2008 12 21

How to Run WEKA Demo SVM in WEKA T. B. Chen 2008 12 21

Download- WEKA • Web pages of WEKA as below: http: //www. cs. waikato. ac.

Download- WEKA • Web pages of WEKA as below: http: //www. cs. waikato. ac. nz/ml/weka/

The Flow Chart of Running SVM in WEKA Prepared a training dataset Opening WEKA

The Flow Chart of Running SVM in WEKA Prepared a training dataset Opening WEKA Software Selected Test Options Selected Response Cross-validation Folds = Observations Response should be categorical variable. Results Opening A Training Dataset Selected SVM module in WEKA Choosing proper parameters in SVM Prediction information Perdition error rates, confusion matrix, model estimators,

Open an Training Data with CSV Format (Made by Excel) 1 3 3 2

Open an Training Data with CSV Format (Made by Excel) 1 3 3 2 4

Selected Classifier in WEKA Choose classifier Number of observations Variables in training data.

Selected Classifier in WEKA Choose classifier Number of observations Variables in training data.

Choose SVM in WEKA

Choose SVM in WEKA

Choose Parameters in SVM with Information of Parameters Using left bottom of mouse to

Choose Parameters in SVM with Information of Parameters Using left bottom of mouse to click the white bar to show parameters window. Pushing “more” show the definitions of parameter.

Running SVM in WEKA fro Training Data SVM module with learning parameters If numbers

Running SVM in WEKA fro Training Data SVM module with learning parameters If numbers of fold = numbers of observation, then called “leave-one-out”. Running results Selected the response variables Start running Running results

Weka In C • Requirements – WEKA http: //www. cs. waikato. ac. nz/ml/weka/ –

Weka In C • Requirements – WEKA http: //www. cs. waikato. ac. nz/ml/weka/ – JAVA: (Free Download) http: //www. java. com/zh_TW/download/index. j sp – A C/C++ compiler • DEV C++ • VC++ • Others

Demo NNge Run In C • NNge: (Nearest-neighbor-like algorithm) • 1 st step: Full

Demo NNge Run In C • NNge: (Nearest-neighbor-like algorithm) • 1 st step: Full name of Nneg. [Name: weka. classifiers. rules. NNge] • 2 nd step: Understanding parameters of Nneg from Weka. • 3 rd step: Command line syntax java -cp C: /Progra~1/Weka-3 -4/weka. jar weka. classifiers. rules. NNge -G 5 -I 3 -t C: /Progra~1/Weka-3 -4/data/weather. arff -x 10

Command line syntax JAVA file for Weka • Command line syntax: C: >java -cp

Command line syntax JAVA file for Weka • Command line syntax: C: >java -cp C: /Progra~1/Weka-3 -4/weka. jar weka. classifiers. rules. NNge -G 5 -I 3 -t C: /Progra~1/Weka-3 -4/data/weather. arff -x 10 Full name of NNge in Weka Training data must save as *. arff - Description: -t filename: Training data input -G 5: Sets the number of attempts for generalization is 5. -I 3: Sets the number of folder for mutual information is 3. -x 10: 10 -folds cross-validation

Example C File • char Syn. Str[512]; //Create String Variable • sprintf(Syn. Str, "java

Example C File • char Syn. Str[512]; //Create String Variable • sprintf(Syn. Str, "java -cp C: /Progra~1/Weka-3 -4/weka. jar weka. classifiers. rules. NNge -G %d -I %d -t %s -x %d > List. txt", i. G, i. I, argv[1], i. X); //Print Command line syntax to Syn. Str • system(Syn. Str); //Now, Using system() to run it. Viewing a Demo C Codes

Enjoy It! ^____^

Enjoy It! ^____^

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