public class MISVM extends Classifier implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler
@inproceedings{Andrews2003, author = {Stuart Andrews and Ioannis Tsochantaridis and Thomas Hofmann}, booktitle = {Advances in Neural Information Processing Systems 15}, pages = {561-568}, publisher = {MIT Press}, title = {Support Vector Machines for Multiple-Instance Learning}, year = {2003} }Valid options are:
-D If set, classifier is run in debug mode and may output additional info to the console
-C <double> The complexity constant C. (default 1)
-N <default 0> Whether to 0=normalize/1=standardize/2=neither. (default: 0=normalize)
-I <num> The maximum number of iterations to perform. (default: 500)
-K <classname and parameters> The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)
Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
-D Enables debugging output (if available) to be printed. (default: off)
-no-checks Turns off all checks - use with caution! (default: checks on)
-C <num> The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)
-E <num> The Exponent to use. (default: 1.0)
-L Use lower-order terms. (default: no)
SMO
,
Serialized FormModifier and Type | Field and Description |
---|---|
static int |
FILTER_NONE
No normalization/standardization
|
static int |
FILTER_NORMALIZE
Normalize training data
|
static int |
FILTER_STANDARDIZE
Standardize training data
|
static Tag[] |
TAGS_FILTER
The filter to apply to the training data
|
Constructor and Description |
---|
MISVM() |
Modifier and Type | Method and Description |
---|---|
void |
buildClassifier(Instances train)
Builds the classifier
|
java.lang.String |
cTipText()
Returns the tip text for this property
|
double[] |
distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar
|
java.lang.String |
filterTypeTipText()
Returns the tip text for this property
|
double |
getC()
Get the value of C.
|
Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
|
SelectedTag |
getFilterType()
Gets how the training data will be transformed.
|
Kernel |
getKernel()
Gets the kernel to use.
|
int |
getMaxIterations()
Gets the maximum number of iterations.
|
Capabilities |
getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the
relational data.
|
java.lang.String[] |
getOptions()
Gets the current settings of the classifier.
|
java.lang.String |
getRevision()
Returns the revision string.
|
TechnicalInformation |
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
|
java.lang.String |
globalInfo()
Returns a string describing this filter
|
java.lang.String |
kernelTipText()
Returns the tip text for this property
|
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options
|
static void |
main(java.lang.String[] argv)
Main method for testing this class.
|
java.lang.String |
maxIterationsTipText()
Returns the tip text for this property
|
void |
setC(double v)
Set the value of C.
|
void |
setFilterType(SelectedTag newType)
Sets how the training data will be transformed.
|
void |
setKernel(Kernel value)
Sets the kernel to use.
|
void |
setMaxIterations(int value)
Sets the maximum number of iterations.
|
void |
setOptions(java.lang.String[] options)
Parses a given list of options.
|
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
public static final int FILTER_NORMALIZE
public static final int FILTER_STANDARDIZE
public static final int FILTER_NONE
public static final Tag[] TAGS_FILTER
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class Classifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-D If set, classifier is run in debug mode and may output additional info to the console
-C <double> The complexity constant C. (default 1)
-N <default 0> Whether to 0=normalize/1=standardize/2=neither. (default: 0=normalize)
-I <num> The maximum number of iterations to perform. (default: 500)
-K <classname and parameters> The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)
Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
-D Enables debugging output (if available) to be printed. (default: off)
-no-checks Turns off all checks - use with caution! (default: checks on)
-C <num> The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)
-E <num> The Exponent to use. (default: 1.0)
-L Use lower-order terms. (default: no)
setOptions
in interface OptionHandler
setOptions
in class Classifier
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class Classifier
public java.lang.String kernelTipText()
public Kernel getKernel()
public void setKernel(Kernel value)
value
- the kernelpublic java.lang.String filterTypeTipText()
public void setFilterType(SelectedTag newType)
newType
- the new filtering modepublic SelectedTag getFilterType()
public java.lang.String cTipText()
public double getC()
public void setC(double v)
v
- Value to assign to C.public java.lang.String maxIterationsTipText()
public int getMaxIterations()
public void setMaxIterations(int value)
value
- the maximum number of iterations.public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class Classifier
Capabilities
public Capabilities getMultiInstanceCapabilities()
getMultiInstanceCapabilities
in interface MultiInstanceCapabilitiesHandler
Capabilities
public void buildClassifier(Instances train) throws java.lang.Exception
buildClassifier
in class Classifier
train
- the training data to be used for generating the
boosted classifier.java.lang.Exception
- if the classifier could not be built successfullypublic double[] distributionForInstance(Instance exmp) throws java.lang.Exception
distributionForInstance
in class Classifier
exmp
- the exemplar for which distribution is computedjava.lang.Exception
- if the distribution can't be computed successfullypublic java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(java.lang.String[] argv)
argv
- should contain the command line arguments to the
scheme (see Evaluation)