| Modifier and Type | Class and Description |
|---|---|
class |
AbstractClassifier
Abstract base class for classifiers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultinomialNaiveBayes
Multinomial naive bayes classifier.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractKNearestNeighbours
K nearest neighbour classification algorithm that is seeded with a "database"
of known examples and predicts based on the k-nearest neighbours majority
vote for a class.
|
class |
KNearestNeighbours
K nearest neighbour classification algorithm that is seeded with a "database"
of known examples and predicts based on the k-nearest neighbours majority
vote for a class.
|
class |
SparseKNearestNeighbours
K nearest neighbour classification algorithm that is seeded with a "database"
of known examples and predicts based on the k-nearest neighbours majority
vote for a class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Voter<A extends Classifier>
Implementation of vote ensembling.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultilayerPerceptron
Multilayer perceptron implementation that works on GPU via JCuda and CPU.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LogisticRegression |
| Modifier and Type | Class and Description |
|---|---|
class |
DecisionTree
A decision tree that can be used for classification with numerical or
categorical features.
|
class |
RandomForest
A decision tree forest, using bagging.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MaxEntMarkovModel
Maximum entropy markov model for named entity recognition (classifying labels
in sequence learning).
|
| Modifier and Type | Class and Description |
|---|---|
class |
HMM
Hidden Markov Model implementation for multiple observations for all three
types of problems HMM aims to solve (Decoding, likelihood estimation,
unsupervised/supervised learning).
|
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