类 MISVM

所有已实现的接口:
Serializable, Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). Applying weka.classifiers.functions.SMO to solve multiple instances problem.
The algorithm first assign the bag label to each instance in the bag as its initial class label. After that applying SMO to compute SVM solution for all instances in positive bags And then reassign the class label of each instance in the positive bag according to the SVM result Keep on iteration until labels do not change anymore.

For more information see:

Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems 15, 561-568, 2003.

BibTeX:

 @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)
版本:
$Revision: 9144 $
作者:
Lin Dong (ld21@cs.waikato.ac.nz)
另请参阅:
  • 字段详细资料

    • FILTER_NORMALIZE

      public static final int FILTER_NORMALIZE
      Normalize training data
      另请参阅:
    • FILTER_STANDARDIZE

      public static final int FILTER_STANDARDIZE
      Standardize training data
      另请参阅:
    • FILTER_NONE

      public static final int FILTER_NONE
      No normalization/standardization
      另请参阅:
    • TAGS_FILTER

      public static final Tag[] TAGS_FILTER
      The filter to apply to the training data
  • 构造器详细资料

    • MISVM

      public MISVM()
  • 方法详细资料

    • globalInfo

      public String globalInfo()
      Returns a string describing this filter
      返回:
      a description of the filter suitable for displaying in the explorer/experimenter gui
    • getTechnicalInformation

      public 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.
      指定者:
      getTechnicalInformation 在接口中 TechnicalInformationHandler
      返回:
      the technical information about this class
    • listOptions

      public Enumeration listOptions()
      Returns an enumeration describing the available options
      指定者:
      listOptions 在接口中 OptionHandler
      覆盖:
      listOptions 在类中 Classifier
      返回:
      an enumeration of all the available options
    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.

      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)
      指定者:
      setOptions 在接口中 OptionHandler
      覆盖:
      setOptions 在类中 Classifier
      参数:
      options - the list of options as an array of strings
      抛出:
      Exception - if an option is not supported
    • getOptions

      public String[] getOptions()
      Gets the current settings of the classifier.
      指定者:
      getOptions 在接口中 OptionHandler
      覆盖:
      getOptions 在类中 Classifier
      返回:
      an array of strings suitable for passing to setOptions
    • kernelTipText

      public String kernelTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • getKernel

      public Kernel getKernel()
      Gets the kernel to use.
      返回:
      the kernel
    • setKernel

      public void setKernel(Kernel value)
      Sets the kernel to use.
      参数:
      value - the kernel
    • filterTypeTipText

      public String filterTypeTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • setFilterType

      public void setFilterType(SelectedTag newType)
      Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
      参数:
      newType - the new filtering mode
    • getFilterType

      public SelectedTag getFilterType()
      Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
      返回:
      the filtering mode
    • cTipText

      public String cTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • getC

      public double getC()
      Get the value of C.
      返回:
      Value of C.
    • setC

      public void setC(double v)
      Set the value of C.
      参数:
      v - Value to assign to C.
    • maxIterationsTipText

      public String maxIterationsTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • getMaxIterations

      public int getMaxIterations()
      Gets the maximum number of iterations.
      返回:
      the maximum number of iterations.
    • setMaxIterations

      public void setMaxIterations(int value)
      Sets the maximum number of iterations.
      参数:
      value - the maximum number of iterations.
    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      指定者:
      getCapabilities 在接口中 CapabilitiesHandler
      覆盖:
      getCapabilities 在类中 Classifier
      返回:
      the capabilities of this classifier
      另请参阅:
    • getMultiInstanceCapabilities

      public Capabilities getMultiInstanceCapabilities()
      Returns the capabilities of this multi-instance classifier for the relational data.
      指定者:
      getMultiInstanceCapabilities 在接口中 MultiInstanceCapabilitiesHandler
      返回:
      the capabilities of this object
      另请参阅:
    • buildClassifier

      public void buildClassifier(Instances train) throws Exception
      Builds the classifier
      指定者:
      buildClassifier 在类中 Classifier
      参数:
      train - the training data to be used for generating the boosted classifier.
      抛出:
      Exception - if the classifier could not be built successfully
    • distributionForInstance

      public double[] distributionForInstance(Instance exmp) throws Exception
      Computes the distribution for a given exemplar
      覆盖:
      distributionForInstance 在类中 Classifier
      参数:
      exmp - the exemplar for which distribution is computed
      返回:
      the distribution
      抛出:
      Exception - if the distribution can't be computed successfully
    • getRevision

      public String getRevision()
      Returns the revision string.
      指定者:
      getRevision 在接口中 RevisionHandler
      覆盖:
      getRevision 在类中 Classifier
      返回:
      the revision
    • main

      public static void main(String[] argv)
      Main method for testing this class.
      参数:
      argv - should contain the command line arguments to the scheme (see Evaluation)