Greedy Function Approximation

Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189-1232.

Type

Journal article

Intellectual base

Primary research

Year

2001

DOI

10.1214/aos/1013203451

Link

https://doi.org/10.1214/aos/1013203451

Cited from

statistics_experimental_design

Cited by

2 citations across 2 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Residual Analysis
  • Medicine: a clinician's leading hypothesis is the model, and the symptoms it fails to explain are the residual that drives differential-diagnosis refinement. Machine learning: gradient boosting fits successive weak learners to the residuals of prior learners; residual connections instantiate the move at the architecture level.

    This sourceFormalizes gradient boosting as the fitting of successive weak learners to the residuals of prior learners, converging when residuals stop carrying exploitable pattern.

Domain-specific

• Boosting
  • … equivalence of weak and strong learnability and gave a boosting construction. AdaBoost made the correction adaptive through multiplicative example weights and a weighted vote. Gradient boosting generalized the pattern as stagewise functional optimization, fitting new components to descent directions for a chosen loss.

Verification

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