Some examples and use cases from the internet:

Some examples and use cases from the internet:

• 0.4 and a momentum term equal to 0.2. Once we had selected the best combination of topology, learning algorithm and parameters for the MLP, according to.
• Dec 18, 2006 ... A MLP network (with learning rate equal to 0.2 and a momentum term equal to 0.3) with three 3–18 inputs, 2 hidden layers and 3 outputs ...
• Oct 16, 2002 ... A MLP network (with learning rate equal to 0.2 and a momentum term equal to 0.3) with 3–32 inputs and 6 output neurons was able to reach a ...
• Oct 16, 2002 ... A MLP network (with learning rate equal to 0.2 and a momentum term equal to 0.3) with 3–32 inputs and 6 output neurons was able to reach a ...

• '.
• ... policy learned by BayesiaLab over 1 000 time steps, with a discount factor equal to 0.99, a learning rate equal to 0.5 and an initial exploration rate equal to 1.
• ... threshold equal to 0 and learning rate equal to 0.5; the noise is equal to 0.5, which ... players with aspiration threshold equal to 2 and learning rate equal to 0.5.
• l + kk RL!lffkXk,. ( E.12) where kk is a time and data dependent scalar learning rate equal to p
• The weight update formula could also be rewritten as. Wk = Wk
• learning rate equal to the inverse of the largest eigenvalue of the Hessian. This result of course assume that these learning rates still fulfill criterions (4.33) and.