4 ms·

Isn't this just bagging and boosting?

• PaulHoule 7y ago
  No. This approach affects how you build training sets. Beyond a certain point there is no benefit from adding more "easy" cases to the training set. If you're going to build something that handles the hard cases you need to build tranches of examples that cover the hard cases, and the methodology I'm describing requires that.
  • Der_Einzige 7y ago
    Your answer doesn't explain to me how it's meaningfully different from boosting and bagging. Boosting finds the "hard" datapoints by figuring out what the early weak learners fail on, and then trains further weak learners by boosting the weights of those "hard" datapoints in the training data. Sounds just like what you described How is the method used by the "military industrial complex" meaningfully different?
    • PaulHoule 7y ago
      Because you craft the training set to add more hard cases. The trouble with mainstream machine learners is that they're allergic to making their own training sets. If they can't download it from Kaggle, it doesn't exist. No wonder they (at best) get the same results as everyone else. It drives me nuts. Consider those digits that LeCunn, Hinton and other have popularized. Every seen somebody try it with a different set of symbols? People assume that the same methods would work with a different set of symbols, but the systematic avoidance of anyone showing that results suggests strongly that it wouldn't work with other symbols. The "military industrial complex" folks build and use NLP systems every day that are much better than the "state of the art" in NN-based NLP, and it is as much about designing the tasks (e.g. NOT Penn Treebank!) and sculpting training sets as it is anything else.