.png){kind=icon}
Return
Learning Learning Curves
.png){kind=icon}
PRE
AI
DOI：10.1007/s10044-024-01394-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Learning curves depict how a model's expected performance changes with varying training set sizes, unlike training curves, showing a gradient-based model's performance with respect to training epochs. Extrapolating learning curves can be useful for determining the performance gain with additional data. Parametric functions, that assume monotone behaviour of the curves, are a prevalent methodology to model and extrapolate learning curves. However, learning curves do not necessarily follow a specific parametric shape: they can have peaks, dips, and zigzag patterns. These unconventional shapes can hinder the extrapolation performance of commonly used parametric curve-fitting models. In addition, the objective functions for fitting such parametric models are non-convex, making them initialization-dependent and brittle. In response to these challenges, we propose a convex, data-driven approach that extracts information from available learning curves to guide the extrapolation of another targeted learning curve. Our method achieves this through using a learning curve database. Using the initial segment of the observed curve, we determine a group of similar curves from the database and reduce the dimensionality via Functional Principle Component Analysis FPCA. These principal components are used in a semi-parametric kernel ridge regression (SPKR) model to extrapolate targeted curves. The solution of the SPKR can be obtained analytically and does not suffer from initialization issues. To evaluate our method, we create a new database of diverse learning curves that do not always adhere to typical parametric shapes. Our method performs better than parametric non-parametric learning curve-fitting methods on this database for the learning curve extrapolation task.
Keywords:
Meta-learning
Data-driven modeling
Learning curves
Extrapolation
Kernel methods
functional data analysis
Journal
IF:
2
Papers:
2.0K
Citations:
1.9K
Organization
Cited Papers
Comparative Seed Dispersal Effectiveness of Sympatric Alouatta guariba and Brachyteles arachnoides in Southeastern Brazil1.png)
Biotropica
BiotropicaIF0
PREAI
ShareSaveShareSaveShareSaveShareSave
View more