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0 pointsLegend24401mo ago0 comments

Ehhh KNN doesn’t have a training phase, so it’s really more that the concept of continual learning doesn’t apply. You have to store your entire dataset and recalculate everything from scratch every time anyway.

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snovv_crash1mo ago

Yes, that's basically the point. You get 'free' continuous learning just by throwing the new data into the pool. Needing an explicit training step is a weakness that makes CL hard to make work for many other approaches.

For any practical application KNN will need some kind of accelerated search structure (eg Kd-tree for < ~7 dimensions) which then requires support for dynamic insertions. But this is an engineering problem, not a data science problem, it works and is practical. For example this has been used by the top systems in Robocode for 15+ years at this point, it's just academia that doesn't find this approach novel enough to bother pursuing.

Legend2440OP1mo ago

>Needing an explicit training step is a weakness that makes CL hard to make work for many other approaches.

On the other hand, not having an explicit training step is a huge weakness of KNN.

Training-based methods scale better because the storage and runtime requirements are independent of dataset size. You can compress 100TB of training data down into a 70GB LLM.

A KNN on the same data would require keeping around the full 100TB, and it would be intractably slow.

snovv_crash1mo ago

Feature engineering is a thing, you don't need the full data source for KNN to do the search in. It is already used extensively in RAG type lookup systems, for example.

j / k navigate · click thread line to collapse

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snovv_crash1mo ago

Legend2440OP1mo ago

>Needing an explicit training step is a weakness that makes CL hard to make work for many other approaches.

On the other hand, not having an explicit training step is a huge weakness of KNN.

Training-based methods scale better because the storage and runtime requirements are independent of dataset size. You can compress 100TB of training data down into a 70GB LLM.

A KNN on the same data would require keeping around the full 100TB, and it would be intractably slow.

snovv_crash1mo ago

Feature engineering is a thing, you don't need the full data source for KNN to do the search in. It is already used extensively in RAG type lookup systems, for example.

j / k navigate · click thread line to collapse