The IDLmlSupportVectorMachineClassification class implements an SVM model that can be used for classification purposes. The following steps are described here.
For a detailed example of how to use the IDL Machine Learning Framework to train a model that learns to recognize hand-written digits, compile and run classify_digits.pro, which is located in the examples/machine_learning directory of your IDL installation.
Example
read_seeds_example_data, data, labels, $
N_ATTRIBUTES=nAttributes, N_EXAMPLES=nExamples, $
N_LABELS=nLabels, UNIQUE_LABELS=uniqueLabels
IDLmlShuffle, data, labels
Normalizer = IDLmlVarianceNormalizer(data)
Normalizer.Normalize, data
Part = IDLmlPartition({train:80, test:20}, data, labels)
Classifier = IDLmlSupportVectorMachineClassification(nAttributes, uniqueLabels)
Loss = Classifier.Train(part.train['data'], LABELS=part.train['labels'])
confMatrix= IDLmlTestClassifier(Classifier, part.test['data'], $
part.test['labels'], ACCURACY=accuracy)
Print, 'Model accuracy:', accuracy
Print, Classifier.Classify(data[*,0])
Syntax
Result = IDLmlSupportVectorMachineClassification(Nattributes, Outputs [, Keywords=Value])
Arguments
Nattributes
Specify the number of attributes that the input data will be required to have.
Outputs
Specify the array of possible outputs. It can be an array of numbers or strings. Outputs can also be a scalar number; in that case, the possible output values will be all integer numbers from 0 to 'outputs' minus one.
Keywords
KERNEL (optional)
Set this keyword to a valid kernel object (IDLmlSVM*Kernel) that encapsulates several SVM parameters. Valid options include IDLmlSVMLinearKernel, IDLmlSVMPolynomialKernel, IDLmlSVMRadialKernel (default), and IDLmlSVMSigmoidKernel.
PENALTY (optional)
Set this keyword to a value indicating the penalty parameter. The default value is 100.0. The optimal value for any particular model is highly variable and will most likely need to be adjusted. You should perform a grid search for parameter selection as described in Hsu, Chang, and Lin (2010).
References
Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Hsu, C.-W., C.-C. Chang, and C.-J. Lin. (2010). A practical guide to support vector classification. National Taiwan University. http://ntu.csie.org/~cjlin/papers/guide/guide.pdf.
Wu, T.-F., C.-J. Lin, and R. C. Weng. (2004). Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5:975-1005, http://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf.
PROBABILITY (optional)
Set this keyword to train the model for probability estimates. The default is 1 for classification problems.
SHRINK (optional)
Set this keyword to ask the model to use the shrinking heuristics. The default is 1.
Properties
CLASS_MAP
A hash that maps internal classification values to desired labels, if the model was defined using custom labels.
KERNEL
The kernel object used to define the model.
NATTRIBUTES
The number of input attributes the model requires.
NOUTPUTS
The number of possible outputs.
OUTPUTS
An array of possible outputs.
PENALTY
The penalty parameter used in the model.
Methods
IDLmlSupportVectorMachineClassification::Classify
The IDLmlSupportVectorMachineClassification::Classify method assigns each example to an output class, returning an array of label results.
Syntax
Result = Obj->[IDLmlSupportVectorMachineClassification::]Classify(Features [, Keywords=Value])
Return Value
The method returns an array of class values that correspond to the data provided.
Arguments
Features
Specify an array of features of size n x m, where n is the number of attributes and m is the number of examples.
Keywords
LOSS (optional)
Set this keyword to a variable that will contain the loss result, which is a unitless number that indicates how closely the model fits the training data.
SCORES (optional)
Set this keyword to an array of size m, where m is the number of examples containing the actual scores associated with the features. Use this keyword to pass in the actual scores associated with the features if you want to calculate the loss.
UNMAPPED_CLASSES (optional)
Set this keyword to a variable that will contain the actual internal class values for the classification results.
IDLmlSupportVectorMachineClassification::Evaluate
The IDLmlSupportVectorMachineClassification::Evaluate method evaluates a number of features and returns an array of scores that represent how closely each feature matches each output.
Syntax
Result = Obj->[IDLmlSupportVectorMachineClassification::]Evaluate(Features [, Keywords=Value])
Return Value
This method returns the scores associated with the features. Scores represent the actual numerical outputs obtained by the model in response to a number of inputs.
Arguments
Features
Specify an array of features of size n x m, where n is the number of attributes and m is the number of examples.
Keywords
LOSS (optional)
Set this keyword to a variable that will contain the loss result, which is a unitless number that indicates how closely the model fits the training data.
SCORES (optional)
Set this keyword to an array of size m, where m is the number of examples containing the actual scores associated with the features. Use this keyword to pass in the actual scores associated with the features if you want to calculate the loss.
IDLmlSupportVectorMachineClassification::Restore
The IDLmlSupportVectorMachineClassification::Restore static method restores the classifier from a file.
Syntax
Result = IDLmlSupportVectorMachineClassification.Restore(Filename)
Return Value
A reference to the object instance restored from the file.
Arguments
Filename
Specify the name of the file to restore.
Keywords
None
IDLmlSupportVectorMachineClassification::Save
The IDLmlSupportVectorMachineClassification::Save method saves the classifier to a file.
Syntax
Obj->[IDLmlSupportVectorMachineClassification::]Save, Filename
Arguments
Filename
Specify the name of the file to save.
Keywords
None
IDLmlSupportVectorMachineClassification::Train
The IDLmlSupportVectorMachineClassification::Train method performs training on the model and the loss, which is a unitless number that indicates how closely the model fits the training data. Unlike all other models, IDLmlSupportVectorMachineClassification only needs to be trained once. Calling this method multiple times will not result in a better trained model.
Syntax
Result = Obj->[IDLmlSupportVectorMachineClassification::]Train(Features [, Keywords=Value])
Return Value
This method returns the loss, which is a unitless number that indicates how closely the model fits the training data.
Arguments
Features
Specify an array of features of size n x m, where n is the number of attributes and m is the number of examples.
Keywords
CALLBACK_FUNCTION (optional)
An optional string with the name of an IDL function to be called on each training iteration. The callback function must accept two arguments: loss and state. The callback function must return 1 (!true) if the training should perform another iteration, or 0 (!false) if it should stop training.
LABELS (optional)
An array of size m, where m is the number of examples containing the actual labels associated with the features.
SCORES (optional)
An array of size m, where m is the number of examples containing the actual scores associated with the features.
TRAIN_STATE (optional)
Specify optional user data to provide for the callback function.
Version History
See Also
IDLmlAutoEncoder, IDLmlFeedForwardNeuralNetwork, IDLmlKMeans, IDLmlSoftmax, IDLmlSupportVectorMachineRegression, IDLmlTestClassifier