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>You can correct many of it's errors with simple language models. For instance the phrase "wreck a nice beach" has much less probability than "recognize speech"
by rm999 10y ago
>You can correct many of it's errors with simple language models. For instance the phrase "wreck a nice beach" has much less probability than "recognize speech". So if the model isn't sure which one it is, you can assume it's the more probable one. Then train on that, and it will get even better at recognizing those words.
Why not just build that heuristic into your initial supervised "weak" model? Training on data labeled by a model introduces no new information, so you're not gaining anything there.
- Houshalter 10y agoIt does introduce new information. This is how semi-supervised learning works. Many words may be missed by the weak model, but can be inferred correctly from their context. Then you have new labels to train the weak model on, to make it better at those words. The way I'm describing it is probably not the optimal way to do it. I don't know if there is a better way. But the point is it must be possible to take advantage of the vast quantities of unlabelled data we have. Humans brains somehow do something similar. Semi supervised learning is really cool. I saw one successful example where they labelled just a few emails as spam and not spam. Then they trained used their weak classifier to label thousands of unclassified emails, and then used those as training data for an even stronger model. It actually works: http://matpalm.com/semi_supervised_naive_bayes/semi_supervised_bayes.html http://matpalm.com/semi_supervised_naive_bayes/semi_supervis... https://en.wikipedia.org/wiki/Semi-supervised_learning https://en.wikipedia.org/wiki/Semi-supervised_learning