FITTING SURGE FUNCTIONS TO DATA

Fitting Aggregation Functions to Data: Part I - Linearization and Regularization

The use of supervised learning techniques for fitting weights and/or generator functions of weighted quasi-arithmetic means – a special class of idempotent and nondecreasing aggregation functions – to empirical data has already been considered in a number of papers. Nevertheless, there are still some important issues that have not been discussed in the literature yet. In the first part of this ...

Fitting Aggregation Functions to Data: Part II - Idempotization

The use of supervised learning techniques for fitting weights and/or generator functions of weighted quasi-arithmetic means – a special class of idempotent and nondecreasing aggregation functions – to empirical data has already been considered in a number of papers. Nevertheless, there are still some important issues that have not been discussed in the literature yet. In the second part of this...

Superellipse fitting to partial data

Superellipses can be used to represent in a compact form a large variety of shapes, and are useful for modelling in the fields of computer graphics and computer vision. However, fitting them to data is difficult and computationally expensive. Moreover, when only partial data is available the parameter estimates become unreliable. This paper attempts to improve the process of fitting to partial ...

Fitting Models to Grouped Data

Fitting Ranked Linguistic Data with Two-Parameter Functions

It is well known that many ranked linguistic data can fit well with one-parameter models such as Zipf’s law for ranked word frequencies. However, in cases where discrepancies from the one-parameter model occur (these will come at the two extremes of the rank), it is natural to use one more parameter in the fitting model. In this paper, we compare several two-parameter models, including Beta fun...