Stochastic gradient descent
Algorithms AIMech:StochasticGradientDescent DRAFT
Aliases: SGD
Optimization using gradients estimated from sampled training observations.
Identity Notes & Concept Scope
TrainingStrategy does not identify a stochastic optimization algorithm, and its preprocessing genus requires upstream clarification.
Claims & Grounded Evidence
1 Claim
Mechanism
#mechanism
Parameters are updated using an estimated objective gradient computed from sampled training observations.
⚠️ Curated Limitations & Negative Results
- This record supports method identity and the stated mechanism only; no universal performance or biological efficacy claim is curated.
- Proposed placement is an inference from the existing AIO hierarchy; maintainers must resolve task, algorithm and model scope before acceptance. These are procedures, not trained model instances; current AIO branch definitions mix abstraction levels.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:TrainingStrategy
TrainingStrategy
|
related_match | TrainingStrategy does not identify a stochastic optimization algorithm, and its preprocessing genus requires upstream clarification. Mapping is local curation, not an upstream subclass assertion. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
scikit-learn developers
|
official documentation
documentation
|
sha256:e487031917c5…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T03:01:34Z
Updated: 2026-10-11T03:01:34Z
Source File:
data/curated/StochasticGradientDescent.yaml