diva.sketch.classification
Class WeightedEuclideanClassifier

java.lang.Object
  extended by diva.sketch.classification.AbstractClassifier
      extended by diva.sketch.classification.WeightedEuclideanClassifier
All Implemented Interfaces:
Classifier, TrainableClassifier

public class WeightedEuclideanClassifier
extends AbstractClassifier

WeightedEuclideanClassifier is a trainable classifier that uses a weighted N-dimensional distance between feature sets to classify its input.

Version:
$Revision: 1.7 $
Author:
Heloise Hse (hwawen@eecs.berkeley.edu), Michael Shilman (michaels@eecs.berkeley.edu)

Field Summary
protected static double MIN_SIGMA
          The minimum sigma value; used to avoid divide-by-zero errors.
protected static double NORMALIZATION
          A normalization constant: 10 divided by 30, every 30 unit in distance results in a 10% recognition drop.
 
Fields inherited from class diva.sketch.classification.AbstractClassifier
_weights
 
Constructor Summary
WeightedEuclideanClassifier()
          Construct a classifier with no weight set.
 
Method Summary
 Classification classify(FeatureSet fs)
          Classify the specified feature set using each weight, by comparing them to the mu (mean) value of the weight and weighting it by the sigma value (standard deviation).
 
Methods inherited from class diva.sketch.classification.AbstractClassifier
clear, debug, isIncremental, train
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

MIN_SIGMA

protected static final double MIN_SIGMA
The minimum sigma value; used to avoid divide-by-zero errors.

See Also:
Constant Field Values

NORMALIZATION

protected static final double NORMALIZATION
A normalization constant: 10 divided by 30, every 30 unit in distance results in a 10% recognition drop.

See Also:
Constant Field Values
Constructor Detail

WeightedEuclideanClassifier

public WeightedEuclideanClassifier()
Construct a classifier with no weight set. The weight set is created by the train method.

Method Detail

classify

public Classification classify(FeatureSet fs)
                        throws ClassifierException
Classify the specified feature set using each weight, by comparing them to the mu (mean) value of the weight and weighting it by the sigma value (standard deviation). For each feature f,

    value = sum((input[f] - mu[f])^2/sigma[f]^2)
 
Finally, normalize the value into a confidence between 0 and 100.

Throws:
ClassifierException


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