Specific to this model is that during learning, sets of input features are probabilistically sampled. These sets are represented, in a hidden layer, by ...
Missing: IAK | Show results with:IAK
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A connectionist model for classification learning – the IAK model. In Proceedings of the seventeenth annual conference of the Cognitive. Science Society, 293 ...
Feb 26, 2007 · Here, ai is the activation of the ith unit, neti is its net input, and wis is the weight of the connection from unit s to unit i.
Missing: IAK | Show results with:IAK
Heydemann, A Connectionist Model for Classification Learning - The IAK Model. A. Cleeremans, Implicit Learning in the Presence of Multiple Cues. S.J. ...
There has been recent interest in the idea that principles governing learning in connectionist networks can form the basis for an alternative understanding ...
Missing: IAK | Show results with:IAK
A connectionist model for classification learning – the IAK model. Paper presented at the. Seventeenth Annual Conference of the Cognitive. Science Society ...
The focus of this research is on how people blend knowledge gained through explicit instruction with knowledge gained through experience. The product of.
This means that we need to do more than ask if a connectionist model can in principle show how the classification of plants and animals (or other categories) ...
We present Network Reinforcement Learning (NRL) as more efficient and robust than traditional reinforcement learning in complex environments.
The connectionist model asserts that classifications and confidence are based on the strength of learned associations between symptoms and diseases. The ...
Missing: IAK | Show results with:IAK