Summary of Learning From String Sequences, by David Lindsay and Sian Lindsay
Learning from String Sequences
by David Lindsay, Sian Lindsay
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 Universal Similarity Metric (USM) is a promising tool for measuring similarity between sequence data. By using USM as an alternative distance metric in K-Nearest Neighbours (K-NN) learners, we can effectively recognize patterns in variable length sequence data. In this paper, we compare the USM approach to the commonly used string-to-word vector approach on two datasets from divergent domains: spam email filtering and protein subcellular localization. Our results show that the USM-based K-NN learner outperforms techniques using the string-to-word vector approach in terms of classification accuracy, and can generate reliable probability forecasts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Universal Similarity Metric is a new way to measure how similar two pieces of sequence data are. It helps machines recognize patterns in these sequences more accurately. We tested this method against another popular method on two different types of data: emails that might be spam or not, and where proteins go inside cells. The results show that the new method works better and can make accurate predictions about what’s likely to happen. |
Keywords
» Artificial intelligence » Classification » Probability
