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See:
Description
Interface Summary | |
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Classifier | A Classifier performs generic classification on feature sets, the semantics of which it knows nothing about. |
TrainableClassifier | A Classifier performs generic classification on feature sets, the semantics of which it knows nothing about. |
Class Summary | |
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AbstractClassifier | Given a training set containing multiple classes, for each class, an AbstractClassifier would compute the mu and sigma of each feature of that class. |
BayesClassifier | A naive bayes classifier. |
Classification | Data structure for storing a list of classifer type and confidence value pairs in the order of descending confidence values. |
CrossValidation | |
CrossValidation.CVData | Containing the training set on which the cross-vali |
CrossValidation.CVResult | |
FeatureSet | A data structure for storing features for a classifier; it is basically a typesafe array of doubles with appropriate accessor methods. |
GaussianWeightSet | Given a set of training examples (each example is a feature vector), a Gaussian classifier computes the mu and sigma for each type of features. |
KNNClassifier | A K Nearest Neighbor classifier compares a given example (feature set) to the training set and make its prediction based on the majority match in the top K candidates. |
RubineClassifier | This classifier implements the classic linear discriminator. |
TrainingSet | A TrainingSet contains a set of types, and for each type a corresponding set of positive and negative examples. |
WeightedEuclideanClassifier | WeightedEuclideanClassifier is a trainable classifier that uses a weighted N-dimensional distance between feature sets to classify its input. |
WeightSet | A WeightSet object represents a training type (e.g. |
Exception Summary | |
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ClassifierException | Thrown when there is some internal error in the training or classification process. |
This package provides basic pattern classification. A classifier is trained with a set of examples in the form of feature vectors. Once the classifier is trained, it can classify input feature vectors. The resulting classification is a list of [type, confidence] pairs according to the similarity of the input vector with the examples.
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