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