Summary of Multiple Locally Linear Kernel Machines, by David Picard
Multiple Locally Linear Kernel Machines
by David Picard
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 non-linear classifier is proposed, combining locally linear classifiers in a Multiple Kernel Learning (MKL) framework. The problem is formulated as an _1-regularized optimization task using numerous locally linear kernels, which are then handled by a scalable MKL training algorithm for streaming kernel updates. This approach bridges the gap between high-accuracy but slow non-linear classifiers and fast but low-accuracy linear classifiers in terms of inference time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to classify things that’s not too complicated or too simple. It combines small, local pieces of information to make big decisions. The method is designed to be efficient while still being accurate. This helps solve a common problem where you want something fast and good, but it’s hard to get both. |
Keywords
* Artificial intelligence * Inference * Optimization
