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See:
Description
Interface Summary | |
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Gradient | Provides the ability to inject a gradient into the SGD logistic regresion. |
PriorFunction | A prior is used to regularize the learning algorithm. |
RecordFactory | A record factor understands how to convert a line of data into fields and then into a vector. |
Class Summary | |
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AbstractOnlineLogisticRegression | Generic definition of a 1 of n logistic regression classifier that returns probabilities in response to a feature vector. |
AdaptiveLogisticRegression | This is a meta-learner that maintains a pool of ordinary
OnlineLogisticRegression learners. |
AdaptiveLogisticRegression.TrainingExample | |
AdaptiveLogisticRegression.Wrapper | Provides a shim between the EP optimization stuff and the CrossFoldLearner. |
CrossFoldLearner | Does cross-fold validation of log-likelihood and AUC on several online logistic regression models. |
CsvRecordFactory | Converts CSV data lines to vectors. |
DefaultGradient | Implements the basic logistic training law. |
ElasticBandPrior | Implements a linear combination of L1 and L2 priors. |
GradientMachine | Online gradient machine learner that tries to minimize the label ranking hinge loss. |
L1 | Implements the Laplacian or bi-exponential prior. |
L2 | Implements the Gaussian prior. |
MixedGradient | Provides a stochastic mixture of ranking updates and normal logistic updates. |
ModelDissector | Uses sample data to reverse engineer a feature-hashed model. |
ModelDissector.Weight | |
ModelSerializer | Provides the ability to store SGD model-related objects as binary files. |
OnlineLogisticRegression | Extends the basic on-line logistic regression learner with a specific set of learning rate annealing schedules. |
PassiveAggressive | Online passive aggressive learner that tries to minimize the label ranking hinge loss. |
PolymorphicWritable | Utilities that write a class name and then serialize using writables. |
RankingGradient | Uses the difference between this instance and recent history to get a gradient that optimizes ranking performance. |
TPrior | Provides a t-distribution as a prior. |
UniformPrior | A uniform prior. |
Implements a variety of on-line logistric regression classifiers using SGD-based algorithms. SGD stands for Stochastic Gradient Descent and refers to a class of learning algorithms that make it relatively easy to build high speed on-line learning algorithms for a variety of problems, notably including supervised learning for classification.
The primary class of interest in the this package is
CrossFoldLearner
which contains a
number (typically 5) of sub-learners, each of which is given a different portion of the
training data. Each of these sub-learners can then be evaluated on the data it was not
trained on. This allows fully incremental learning while still getting cross-validated
performance estimates.
The CrossFoldLearner implements OnlineLearner
and thus expects to be fed input in the form
of a target variable and a feature vector. The target variable is simply an integer in the
half-open interval [0..numFeatures) where numFeatures is defined when the CrossFoldLearner
is constructed. The creation of feature vectors is facilitated by the classes that inherit
from FeatureVectorEncoder
.
These classes currently implement a form of feature hashing with
multiple probes to limit feature ambiguity.
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