We suggest the Bayes point machine as a well-founded improvement which approximates the Bayes-optimal decision by the centre of mass of version space. We ...

Infer.NET is a framework for running Bayesian inference in graphical models. It can be used to solve many different kinds of machine learning problems, ...

In this paper we present the Bayes point machine as an approximation to Bayesian inference for linear classifiers in kernel space. In contrast to the Gaussian ...

In this contribution we review the idea of the Bayes point machine (BPM) as an approximation to Bayesian inference for linear classifiers in kernel space in ...

ABSTRACT. We mount eight pressure sensors on a computer mouse and collect mouse pressure signals from subjects who fill out web.

In this paper we present a method based on the simple perceptron learning algorithm which allows to overcome this algorithmic drawback. The method is al(cid:173) ...

A Bayes Point Machine (BPM) classifies an input vector x by: finding the inner product of x with a weight vector w; giving the output y as ...

Oct 8, 2021 · Abstract:A Bayes point machine is a single classifier that approximates the majority decision of an ensemble of classifiers.

The Two Class Bayes Point Machine proved to be effective in predicting loan repayment by achieving an F1 score of .852 on the low credit population of borrowers ...

Bayes Point Machines with Soft Boundaries. To allow for training errors we will introduce the following version space con- ditions in place of those in (2):.