/** * Creates a new instance of RobustRegression * @param iterationLearner * Internal learning algorithm that computes optimal solutions * given the current weightedData. The iterationLearner should operate on * WeightedInputOutputPairs (we have a hard time enforcing this, as many * learning algorithms operate both on InputOutputPairs and * WeightedInputOutputPairs and their prototype is "? extends InputOutputPair") * @param kernelWeightingFunction * Kernel function that provides the weighting for the estimate error, * generally the Kernel should weight accurate estimates higher than * inaccurate estimates. * @param maxIterations The maximum number of iterations * @param tolerance The maximum tolerance * Tolerance before stopping the algorithm */ public KernelWeightedRobustRegression( SupervisedBatchLearner<InputType, OutputType, ?> iterationLearner, Kernel<? super OutputType> kernelWeightingFunction, int maxIterations, double tolerance ) { super( maxIterations ); this.setLearned( null ); this.setTolerance( tolerance ); this.setKernelWeightingFunction( kernelWeightingFunction ); this.setIterationLearner( iterationLearner ); }
/** * Creates a new instance of RobustRegression * @param iterationLearner * Internal learning algorithm that computes optimal solutions * given the current weightedData. The iterationLearner should operate on * WeightedInputOutputPairs (we have a hard time enforcing this, as many * learning algorithms operate both on InputOutputPairs and * WeightedInputOutputPairs and their prototype is "? extends InputOutputPair") * @param kernelWeightingFunction * Kernel function that provides the weighting for the estimate error, * generally the Kernel should weight accurate estimates higher than * inaccurate estimates. * @param maxIterations The maximum number of iterations * @param tolerance The maximum tolerance * Tolerance before stopping the algorithm */ public KernelWeightedRobustRegression( SupervisedBatchLearner<InputType, OutputType, ?> iterationLearner, Kernel<? super OutputType> kernelWeightingFunction, int maxIterations, double tolerance ) { super( maxIterations ); this.setLearned( null ); this.setTolerance( tolerance ); this.setKernelWeightingFunction( kernelWeightingFunction ); this.setIterationLearner( iterationLearner ); }
/** * Creates a new instance of RobustRegression * @param iterationLearner * Internal learning algorithm that computes optimal solutions * given the current weightedData. The iterationLearner should operate on * WeightedInputOutputPairs (we have a hard time enforcing this, as many * learning algorithms operate both on InputOutputPairs and * WeightedInputOutputPairs and their prototype is "? extends InputOutputPair") * @param kernelWeightingFunction * Kernel function that provides the weighting for the estimate error, * generally the Kernel should weight accurate estimates higher than * inaccurate estimates. * @param maxIterations The maximum number of iterations * @param tolerance The maximum tolerance * Tolerance before stopping the algorithm */ public KernelWeightedRobustRegression( SupervisedBatchLearner<InputType, OutputType, ?> iterationLearner, Kernel<? super OutputType> kernelWeightingFunction, int maxIterations, double tolerance ) { super( maxIterations ); this.setLearned( null ); this.setTolerance( tolerance ); this.setKernelWeightingFunction( kernelWeightingFunction ); this.setIterationLearner( iterationLearner ); }