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Great tutorial. I see active learning as a halfway house between supervised learning and reinforcement learning, because requesting labels is an action (as in R
by jmmcd 9y ago
Great tutorial. I see active learning as a halfway house between supervised learning and reinforcement learning, because requesting labels is an action (as in RL), but of a very limited, predefined type.
A lot of problems which we initially model as supervised learning are in reality, in a live situation, more like active learning. Going the whole hog to RL may be unnecessary. But still, bandit-type models may be the right fully general setting.
These topics are very much in the air at the moment with a lot of new interest in bandit models and algorithms.
- mycat 9y agoAre there decent recent implementations in python of these? Or tutorials of some sort?
- charlescearl 9y agoThe vowpal wabbit library provides an active learning setup https://github.com/JohnLangford/vowpal_wabbit/wiki/active_learning.pdf https://github.com/JohnLangford/vowpal_wabbit/wiki/active_le... I think you launch an active learning server, and the Python application interacts with it. You may be able to run everything in Python https://github.com/JohnLangford/vowpal_wabbit/tree/master/python/examples https://github.com/JohnLangford/vowpal_wabbit/tree/master/py.... Here's further description https://github.com/JohnLangford/vowpal_wabbit/wiki/Command-line-arguments#active-learning-options https://github.com/JohnLangford/vowpal_wabbit/wiki/Command-l...