Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting

Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting

Kernel logistic regression (KLR) is a powerful and flexible classification algorithm, which possesses an ability to provide the confidence of class prediction. However, its training—typically carried out by (quasi-)Newton methods—is rather timeconsuming. In this paper, we propose an alternative probabilistic classification algorithm called Least-Squares Probabilistic Classifier (LSPC). KLR mode...

PEDOMODELS FITTING WITH FUZZY LEAST SQUARES REGRESSION

Pedomodels have become a popular topic in soil science and environmentalresearch. They are predictive functions of certain soil properties based on other easily orcheaply measured properties. The common method for fitting pedomodels is to use classicalregression analysis, based on the assumptions of data crispness and deterministic relationsamong variables. In modeling natural systems such as s...

Fitting Distances by Least Squares

Least squares fitting

Technical Note: Review of methods for linear least-squares fitting of data and application to atmospheric chemistry problems C. A. Cantrell National Center for Atmospheric Research, Atmospheric Chemistry Division, 1850 Table Mesa Drive, Boulder, CO 80305, USA Received: 13 February 2008 – Accepted: 21 February 2008 – Published: 1 April 2008 Correspondence to: C. A. Cantrell ([email protected]) P...

Tutorial: Least-Squares Fitting

Least-squares fitting, first developed by Carl Friedrich Gauss, is arguably the most widely used technique in statistical data analysis. It provides a method through which the parameters of a model can be optimised in order to obtain the best fit to a data set through the minimisation of the squared differences between the model and the data. This tutorial document describes the closely associa...