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Related Experiment Video

Updated: Jul 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Building cost functions minimizing to some summary statistics.

M Saerens1

  • 1IRIDIA Laboratory, Université Libre de Bruxelles, B-1050 Bruxelles, Belgium. saerens@ulb.ac.be

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary

This study explores how cost functions impact machine learning model outputs. It uses calculus of variations to ensure models approximate conditional expectation, median, geometric mean, and variance.

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Design and Optimization Strategies of a High-Performance Vented Box
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Last Updated: Jul 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Calculus of Variations

Background:

  • Machine learning models are trained by minimizing cost functions, which measure discrepancies between predicted and actual outputs.
  • The choice of cost function significantly influences the probabilistic interpretation of a trained model's output.
  • Existing methods often focus on specific statistical properties, limiting the model's interpretability.

Purpose of the Study:

  • To investigate the relationship between cost functions and the probabilistic interpretation of machine learning model outputs.
  • To derive conditions on cost functions that ensure approximation of various statistical measures.
  • To explore alternative cost functions for nonlinear regression and discuss regression quantiles.

Main Methods:

  • Utilizing the calculus of variations to derive necessary and sufficient conditions for cost functions.
  • Analyzing the impact of different cost functions on approximating conditional expectation, median, q-quantile, geometric mean, and variance.
  • Applying the derived conditions to nonlinear regression scenarios.

Main Results:

  • Established conditions for cost functions to ensure trained models approximate conditional expectation, median (q-quantile), geometric mean, and variance.
  • Demonstrated the applicability of the calculus of variations method for estimating various summary statistics.
  • Highlighted the potential of least absolute deviations as an alternative to ordinary least squares in nonlinear regression.

Conclusions:

  • The choice of cost function is critical for the probabilistic interpretation of machine learning models.
  • Calculus of variations provides a robust framework for designing cost functions that yield desired statistical properties.
  • This research offers a unified approach to cost function selection for diverse statistical estimations in machine learning.