Summary of A Theory Of Machine Learning, by Jinsook Kim and Jinho Kang
A Theory of Machine Learning
by Jinsook Kim, Jinho Kang
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
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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 machine learning theory challenges traditional assumptions in statistical and computational learning theories by positing that machines learn a function when they successfully compute it, rather than relying on probability convergence or correct calculations. This theory redefines the notion of learning true probabilities, suggesting that obtaining an almost-sure convergence to them is not equivalent to actually computing the true probabilities. The paper provides case studies from natural language processing and macroeconomics to illustrate the implications of this new theory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machines can learn by successfully computing a function, challenging traditional assumptions in machine learning. This new theory says that learning true probabilities isn’t just about getting close or doing the math correctly – it’s about actually calculating the true probabilities. The paper looks at how this works in natural language processing and macroeconomics. |
Keywords
» Artificial intelligence » Machine learning » Natural language processing » Probability
