| Interface | Description |
|---|---|
| DistanceFunction |
Represents a distance function between two
testing examples.
|
| FeatureEntry |
Interface representing a feature
within an example.
|
| LibSVMDataSet.ResultInterpreter |
Method by which to interpret the example result
as an object
|
| TestingExample |
Represents a fixed-size sequence of
features without an associated result.
|
| Class | Description |
|---|---|
| ClassificationGUI |
Demo GUI that provides a list of datasets and classifiers
as well as a run button.
|
| Classifier |
Represents a classifier
|
| DataSet |
Represents a set of examples
|
| DenseExample |
An array-backed example, useful in situations
in which most if not all features are non-zero.
|
| EuclideanDistanceSquared |
Implements the squared euclidean distance
between examples.
|
| Example |
Represents a fixed-size sequence of
features with an associated result.
|
| HiddenPatternDataSet |
A dataset based upon a set of patterns, each
a result object and a set of feature number/value pairs.
|
| LibSVMDataSet |
A dataset from an input stream formatted ala LibSVM
|
| ListDataSet |
ArrayList-backed DataSet implementation
|
| MNISTGUI |
Visualizes a subset of MNIST using a 1-NN classifier
|
| NearestNeighborClassifier |
Implements the 1-NN algorithm (classifies by
finding the closest training example).
|
| SparseExample |
Implements an example by storing index/value pairs,
useful in situations where many/most of the example
features are 0
|
| TieBreaker<T> |
A general mechanism by which to fairly choose
the "winner" in a stream of objects of unknown size
(Related: http://en.wikipedia.org/wiki/Reservoir_sampling).
|
| ZeroRClassifier |
Implements the ZeroR classifier (uses the most
frequent result from the training set as the
result for any testing example)
|
| Exception | Description |
|---|---|
| HiddenPatternDataSet.PatternException |
Exception class for errors adding patterns
|