Mini Paper
Problem
在二维合成线性可分数据上，加入少量标签噪声后，带固定学习率的 perceptron 是否仍然能明显优于常数分类 baseline？
Core Idea
即使存在少量标签翻转，简单 perceptron 仍能在二维合成数据上取得明显高于 majority-class baseline 的 balanced accuracy；但其稳定性会随噪声上升而下降。
Method
- Define the hidden lag variable k and a partially observed sequence x_t with local zeroed-out spans.
- Construct a global non-parametric baseline by searching the lag that minimizes mean absolute disagreement between x_t and x_(t-k) on observed pairs.
- Construct the local-to-global method by slicing the sequence into overlapping windows, estimating a local best lag in each window, and then aggregating these lag votes into one global prediction.
- Treat degradation as part of the protocol: if the full method fails, drop to a smaller problem, then to baseline-only, then to a dry-run or failure note instead of leaving the day blank.
Experimental Setup
- topic_id:
manual-在二维合成线性可分数据上-加入少量标签噪声后-带固定学习率的-perceptro - strategy:
full - case_count:
0 - anomaly:
False
Result
- status:
ok - accuracy:
n/a
Interpretation
The unit stays valuable when the improved method fails because the baseline, the degradation path, and the final reasoning remain explicit.
Artifact References
- task_id:
research-2026-03-11b - result_summary_ref:
agn://artifact/c0a8c848c9364a7f5b2ee12a89d224dc288ed28d9c3487caf72ed5bb69c585ca - code_bundle_ref:
agn://artifact/17f927b54573758cd50d381ea871a004103ec03e6fd37eb1fa4115b6b4b6af35 - archive_ref:
agn://artifact/ac3522013a61af29e532eafed2967200e2242e8143a0aa510df36badbd49a76f - trace_index_ref:
agn://artifact/8d0fce9af7d3306c27edd526c16bb804cb1dd07e28056d47d281a02e7b0db869 - research_repo:
/Users/macstudio_alexxon/Documents/AGN_SelfResearchProtocol