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Actually supervised learning is "learning missing data dimension" by parameter tuning via associative learning rules. Gradient descents are a special case of a
by shakascchen 6y ago
Actually supervised learning is "learning missing data dimension" by parameter tuning via associative learning rules.
Gradient descents are a special case of associative learning rules assuming all data points the same importance.
A type of associative learning rules is Hebbian learning rule.
In the very fundamental we only need associative learning. Of course, for practical application we need diverse tools with different conceptual frameworks to choose for commercial: human resource or cost performance.
- Der_Einzige 6y agoI'd like to see a resurgance in pattern mining and association rule mining. That stuff is do awesome and useful!
- blackbear_ 6y ago> Gradient descents are a special case of associative learning rules assuming all data points the same importance. No. Gradient descent is an optimization method that has nothing to do with learning. It is used to optimize parameterized functions that are said to be "learning", but it's not the only approach. It is also trivially easy, and not uncommon, to use different weights for different data points. Can you clarify what you mean with associative learning?