arXiv (All)
open access
Can Domain Generalization be Guaranteed in Small-Sample Learning?
The small-sample learning problem remains a fundamental challenge in machine learning because limited training data lead to unstable model estimation and generalization. Structural Risk Minimization (SRM) has long been regarded as a principled solution under the classical i.i.d. assumption. However,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Globally optimal representations of low-complexity models: statistical guarantees and algorithms#450493
- An alternative variable projection formulation for small-size separable models#277996
- Generalization in offline RL: The structure is more important than the amount of pessimism#332533
- Optimal Tradeoffs Between Network Size and Parameter Magnitude in Neural Approximation and Minimax Regression#1061345
- M2LG-DG: A Multi-modal Local-Global Domain Generalization Framework for Cross-site Major Depressive Disorder Classification#986959
Record · ID 1144479
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive