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See:
Description
Interface Summary | |
Classifier | A Classifier performs generic classification on feature sets, the semantics of which it knows nothing about. |
TrainableClassifier | This classifier trains on a data set and performs classification based on what it has seen. |
Class Summary | |
Classification | Data structure for storing a list of classifer type and confidence value pairs in the order of descending confidence values. |
DataRep | Representing a collection of data with the means, standard deviations, and variances of the feature components. |
FeatureSet | A data structure for storing features of an example; it is basically a typesafe array of doubles with appropriate accessor methods. |
KNNClassifier | This K-nearest neighbor classifier compares a test example with every example in the training set by computing the normalized Euclidean distance. |
MMDClassifier | Minimum distance classifier measures the normalized Euclidean distance between the test example and each of the training classes. |
SVMClassifier | This class uses libsvm, a SVM software library written in Java, to do SVM classification. |
TrainingSet | A TrainingSet contains a set of types, and for each type a corresponding set of positive and negative examples. |
Exception Summary | |
ClassifierException | Thrown when there is some internal error in the training or classification process. |
This package provides data structures such as feature sets, training sets, as well as a classification framework for facilitating pattern classification tasks. It also includes 3 classifier implementations: SVM, minimum mean distance classifier, and nearest neighbor.
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