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Even if it worked on the specific FPGAs you provide, it might not work reliably in different temperatures like the one in the article, or if the noise is slight
by Houshalter 12y ago
Even if it worked on the specific FPGAs you provide, it might not work reliably in different temperatures like the one in the article, or if the noise is slightly further away, etc.
There is no way to stop overfitting because it's difficult if not impossible to test it in every possible environment we want it to work under.
- TheLoneWolfling 12y agoThere are ways to catch overfitting: https://en.wikipedia.org/wiki/Cross-validation_%28statistics%29 https://en.wikipedia.org/wiki/Cross-validation_%28statistics...
- Houshalter 12y agoThat's not really relevant to this. That is for selecting hyper-parameters for statistical models.
- TheLoneWolfling 12y agoI know the article doesn't mention it, but you can use the exact same techniques for preventing overfitting in machine learning.
- Houshalter 12y agoThat's basically what I said, and this is optimization not machine learning. The problem is the genetic algorithm fits the the specifics of the FPGA and the environment it is optimized in, and doesn't work reliably in other environments or FPGAs.