See: Description
Interface | Description |
---|---|
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 | Description |
---|---|
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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