Re:Deep Learning’s Impact on Image Processing, Mathematics, ...
by YeGoblynQueenne on Sunday May 07, 2017 @11:22AM
(#14285865)
>> [SVM and Decision Trees'] "interpretation" is mostly stuff like "feature 543 and 632 were on"
SVMs are well-known black-box optimisers of numerical parameters so not more or less interpretable than neural nets, but decision tree learners are a different matter. The model they build is symbolic- a propositional logic theorem that can be output in a format that is directly inspectable and interpretable by a human being with an understanding of propositional logic.
It's true that once decision trees grow beyond a certain size they're hard to interpret, but even so they can make a lot more sense than a heap of numbers in a large graph.
Neural nets used for image processing are special in that they can output their activations as images that are directly interpretable by humans, but that's about the only application where we can really tell what a deep network is learning.
Edit:
>> All machine learning models are capable of overfitting.
Aye, but deep nets by definition build extremely complex models with millions of parameters and so are especially prone to overfitting. Bias-vs-variance and all that.
SVMs are well-known black-box optimisers of numerical parameters so not more or less interpretable than neural nets, but decision tree learners are a different matter. The model they build is symbolic- a propositional logic theorem that can be output in a format that is directly inspectable and interpretable by a human being with an understanding of propositional logic.
It's true that once decision trees grow beyond a certain size they're hard to interpret, but even so they can make a lot more sense than a heap of numbers in a large graph.
Neural nets used for image processing are special in that they can output their activations as images that are directly interpretable by humans, but that's about the only application where we can really tell what a deep network is learning.
Edit:
>> All machine learning models are capable of overfitting.
Aye, but deep nets by definition build extremely complex models with millions of parameters and so are especially prone to overfitting. Bias-vs-variance and all that.