Summary of Online Learning Under Haphazard Input Conditions: a Comprehensive Review and Analysis, by Rohit Agarwal et al.
Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis
by Rohit Agarwal, Arijit Das, Alexander Horsch, Krishna Agarwal, Dilip K. Prasad
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper explores the domain of online learning in the context of haphazard inputs, where the input feature space is not constant. It reviews and compares methodologies that can model these inputs, providing code implementations and their environmental impact. The study also categorizes datasets related to haphazard inputs and introduces evaluation metrics for imbalanced datasets. This research aims to advance our understanding of online learning in this specific context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online learning is a growing field with many real-life applications. Usually, we assume that the data input features stay the same, but what happens when they change? This paper looks at how to model these changing inputs and compares different methods for doing so. It also provides code and discusses how this research can help reduce our environmental impact. |
Keywords
* Artificial intelligence * Online learning
