Higher order regression functions result better fit for the calibration curve

Higher order regression functions result better fit for the calibration curve.

Regression analysis is used to predict the dependent variables individually concerning the explanatory available variables and find out the “best fit” line or curve through a series of data in a graph. It is also minimizing the sum of the squares deviations of any experimental data points from the theoretical curve. An important aspect of this analysis is to ensure that the x values are as accu...

Higher Order Functions for Kernel Regression

Kernel regression is a well-established nonparametric method, in which the target value of a query point is estimated using a weighted average of the surrounding training examples. The weights are typically obtained by applying a distance-based kernel function, which presupposes the existence of a distance measure.This paper investigates the use of Genetic Programming for the evolution of task-...

Deforestation for Higher-Order Functions

Higher Order Functions Considered Unnecessary for Higher Order Programming

It is often claimed that the essence of functional programming is the use of functions as values, i.e., of higher order functions, and many interesting examples have been given showing the power of this approach. Unfortunately, the logic of higher order functions is diicult, and in particular, higher order uniication is undecidable. Moreover (and closely related), higher order expressions are n...

Higher-Order Functions for Parsing

In combinator parsing , the text of parsers resembles BNF notation. We present the basic method, and a number of extensions. We address the special problems presented by white– space, and parsers with separate lexical and syntactic phases. In particular, a combining form for handling the “offside rule” is given. Other extensions to the basic method include an “into” combining form with many use...