Summary of Absolute Convergence and Error Thresholds in Non-active Adaptive Sampling, by Manuel Vilares Ferro et al.
Absolute convergence and error thresholds in non-active adaptive sampling
by Manuel Vilares Ferro, Victor M. Darriba Bilbao, Jesús Vilares Ferro
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to building machine learning models is proposed, focusing on non-active adaptive sampling that dynamically derives guaranteed sample size. The paper introduces a method for calculating absolute convergence and error thresholds, enabling decision-making for fine-tuning learning parameters in model selection. This technique proves correct and complete, strengthening the robustness of the sampling scheme, as tested in natural language processing with part-of-speech tagger generation as a case study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are built using data from a training database. A new way to do this is called non-active adaptive sampling. It helps create models that automatically choose the right number of examples. The paper suggests how to calculate when the model’s quality no longer improves and how close it is to achieving that goal. This makes it easier to decide what parameters to adjust for better results. The approach was tested in natural language processing, specifically with part-of-speech tagger generation. |
Keywords
* Artificial intelligence * Fine tuning * Machine learning * Natural language processing
