deep convolutional random forest with residual splits(?)
Considering that random forests have many layers and beat deep learning in most applications, maybe we just need to rebrand them as deep forests and they'll be the next big thing.
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Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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So everybody is crazy because they prefer Deep Learning rather than random forest even the later being better? Give me a break... Random forest scales much worse than Deep Learning to learning high level features from unstructured data such as image, text, video, etc.
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Deep learning is generally better for problems with known a priori structure (vision, language) and worse for those without. Check Kaggle, for example. Everyone should know that.
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You mean “elastic k-NN”?
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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Sure, I mean we should all be surprised that decision trees didn't solve the difficult tasks and weren't behind the likes of alpha fold, multilingual machine translation, image-to-text generation, self-driving cars etc.
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Deep learning is generally better for problems with known a priori structure (vision, language) and worse for those without. Check Kaggle, for example. Everyone should know that.
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Or you're subject to crazy governance rules, so you just use something like C4.5 because you don't want to spend forever getting it approved (or simply talking to risk management people).
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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If by most you mean applications seen on Kaggle or academia where the problems are little toys you might be right. Industry moved past this half a decade ago. Do keep up.
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Clearly you have no idea what Kaggle is.
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Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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