CostParametersType
- The type of parameters that the cost function
takes. For example, a Collection of InputOutputPairs.ResultType
- The type of object created by the learning algorithm.
For example, a FeedforwardNeuralNetwork
.@CodeReview(reviewer="Kevin R. Dixon",date="2008-07-22",changesNeeded=false,comments={"Moved previous code review to annotation.","Interface looks fine."}) @CodeReview(reviewer="Justin Basilico",date="2006-10-03",changesNeeded=false,comments={"Added some missing documentation.","Interface looks fine."}) public interface BatchCostMinimizationLearner<CostParametersType,ResultType> extends BatchLearner<CostParametersType,ResultType>
BatchCostMinimizationLearner
interface defines the functionality
of a cost-minimization learning algorithm should follow.
(These algorithms typically fall into the categories of "supervised" and
"reinforcement" learning algorithms, but I don't like anthropomorphic terms.)
A BatchLearner
takes two generics to parameterize it:
LearnableType
is the class of thing we're going to minimize and the
second parameter CostFunctionType
is a class of CostFunction
that can evaluate the LearnableType
.Modifier and Type | Method and Description |
---|---|
CostFunction<? super ResultType,? super CostParametersType> |
getCostFunction()
Gets the cost function that the learner is minimizing.
|
ResultType |
learn(CostParametersType minimizationParameters)
Invokes the minimization (learning) call using the given cost function
parameters.
|
clone
ResultType learn(CostParametersType minimizationParameters)
learn
in interface BatchLearner<CostParametersType,ResultType>
minimizationParameters
- The parameters for the cost function to
minimize against.CostFunction<? super ResultType,? super CostParametersType> getCostFunction()