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Related Experiment Video

Updated: Jul 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Any reasonable cost function can be used for a posteriori probability approximation.

M Saerens¹, P Latinne, C Decaestecker

¹IRIDIA Lab., Univ. Libre de Bruxelles, Brussels, Belgium.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

This study demonstrates that training machine learning classifiers with reasonable cost functions can yield Bayesian posterior probability estimates. A computable transformation maps classifier outputs to accurate class membership probabilities.

Related Experiment Videos

Last Updated: Jul 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Machine Learning
Pattern Recognition
Subjective Probability Theory

Background:

Machine learning models are typically trained by minimizing a cost function to measure output discrepancies.
The probabilistic interpretation of trained classifier outputs is often unclear.

Purpose of the Study:

To provide a straightforward proof of Lindley's result in subjective probability.
To clarify the probabilistic interpretation of trained classifier outputs in machine learning.
To establish conditions for deriving Bayesian posterior probabilities from classifier outputs.

Main Methods:

Proving Lindley's result within subjective probability theory.
Demonstrating that minimizing a reasonable cost function leads to Bayesian estimation for binary classification.
Deriving necessary conditions for computing output transformations in multi-output cases.

Main Results:

Any reasonable cost function, when minimized, enables Bayesian posterior probability estimation for binary classifiers.
A computable transformation exists to map model outputs to Bayesian posterior probabilities.
Necessary conditions for this transformation were derived for multi-output scenarios.

Conclusions:

The study clarifies the probabilistic interpretation of classifier outputs.
It shows a direct link between minimizing cost functions and achieving Bayesian posterior probability estimation.
Theoretical results are validated with simulation examples.