| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.ensemble |
Provides ensemble methods.
|
| gov.sandia.cognition.learning.algorithm.perceptron |
Provides the Perceptron algorithm and some of its variations.
|
| gov.sandia.cognition.learning.algorithm.tree |
Provides decision tree learning algorithms.
|
| Class and Description |
|---|
| AbstractBaggingLearner
Learns an ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
| AbstractCategorizerOutOfBagStoppingCriteria
Abstract class for implementing a out-of-bag stopping criteria for a
bagging-based ensemble.
|
| AbstractUnweightedEnsemble
An abstract implementation of the
Ensemble interface for
unweighted ensembles. |
| AbstractWeightedEnsemble
An abstract implementation of the
Ensemble interface for ensembles
that have a weight associated with each member. |
| AveragingEnsemble
An ensemble for regression functions that averages together the output value
of each ensemble member to get the final output.
|
| BagBasedCategorizerEnsembleLearner
Interface for a bag-based ensemble learner.
|
| BaggingCategorizerLearner
Learns an categorization ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
| Ensemble
The
Ensemble interface defines the functionality of an "ensemble"
that is typically created by combining together the result of multiple
learning algorithms. |
| IVotingCategorizerLearner
Learns an ensemble in a method similar to bagging except that on each
iteration the bag is built from two parts, each sampled from elements from
disjoint sets.
|
| OnlineBaggingCategorizerLearner
An implementation of an online version of the Bagging algorithm for learning
an ensemble of categorizers.
|
| VotingCategorizerEnsemble
An ensemble of categorizers that determine the result based on an
equal-weight vote.
|
| WeightedBinaryEnsemble
The
WeightedBinaryEnsemble class implements an Ensemble of
BinaryCategorizer objects where each categorizer is assigned a
weight and the category is selected by choosing the one with the largest
sum of weights. |
| WeightedVotingCategorizerEnsemble
An ensemble of categorizers where each ensemble member is evaluated with the
given input to find the category to which its weighted votes are assigned.
|
| Class and Description |
|---|
| WeightedBinaryEnsemble
The
WeightedBinaryEnsemble class implements an Ensemble of
BinaryCategorizer objects where each categorizer is assigned a
weight and the category is selected by choosing the one with the largest
sum of weights. |
| Class and Description |
|---|
| BaggingCategorizerLearner
Learns an categorization ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
| BaggingRegressionLearner
Learns an ensemble for regression by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|