Package | Description |
---|---|
weka.classifiers.bayes | |
weka.classifiers.functions | |
weka.classifiers.lazy | |
weka.classifiers.meta | |
weka.classifiers.rules |
Modifier and Type | Class and Description |
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class |
AODE
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
|
class |
AODEsr
AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see: Fei Zheng, Geoffrey I. |
class |
DMNBtext
Class for building and using a Discriminative Multinomial Naive Bayes classifier.
|
class |
NaiveBayesMultinomialUpdateable
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
|
Modifier and Type | Class and Description |
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class |
SPegasos
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
|
class |
Winnow
Implements Winnow and Balanced Winnow algorithms by Littlestone.
For more information, see N. |
Modifier and Type | Class and Description |
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class |
IB1
Nearest-neighbour classifier.
|
class |
IBk
K-nearest neighbours classifier.
|
class |
KStar
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
|
class |
LWL
Locally weighted learning.
|
Modifier and Type | Class and Description |
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class |
RacedIncrementalLogitBoost
Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets. |
Modifier and Type | Class and Description |
---|---|
class |
NNge
Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
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