Summary of Tensor-based Foundations Of Ordinary Least Squares and Neural Network Regression Models, by Roberto Dias Algarte
Tensor-Based Foundations of Ordinary Least Squares and Neural Network Regression Models
by Roberto Dias Algarte
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel approach to developing Ordinary Least Squares (OLS) and Neural Network regression models by leveraging Tensor Analysis and fundamental matrix computations. This departure from traditional methods in Machine Learning literature yields three algorithms, including a streamlined Backpropagation Algorithm for Neural Networks. The study meticulously details the theoretical foundations of both models and extends them to their complete algorithmic forms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes machine learning more mathematically sound by showing how to develop Ordinary Least Squares and Neural Network regression models using special mathematical tools called Tensor Analysis. It gives a clear understanding of how these models work, making it easier to create new algorithms for things like image recognition and speech recognition. |
Keywords
» Artificial intelligence » Backpropagation » Machine learning » Neural network » Regression
