Learning rule

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. It is done by updating the weights and bias levels of a network when a network is simulated in a specific data environment.[1] A learning rule may accept existing conditions (weights and biases) of the network and will compare the expected result and actual result of the network to give new and improved values for weights and bias.[2] Depending on the complexity of actual model being simulated, the learning rule of the network can be as simple as an XOR gate or mean squared error, or as complex as the result of a system of differential equations.

The learning rule is one of the factors which decides how fast or how accurately the artificial network can be developed. Depending upon the process to develop the network there are three main models of machine learning:

  1. Unsupervised learning
  2. Supervised learning
  3. Reinforcement learning

See also[edit]


  1. ^ Simon Haykin (16 July 1998). "Chapter 2: Learning Processes". Neural Networks: A comprehensive foundation (2nd ed.). Prentice Hall. pp. 50–104. ISBN 978-8178083001. Retrieved 2 May 2012.
  2. ^ S Russell, P Norvig. "Chapter 18: Learning from Examples". Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. pp. 693–859. ISBN 0-13-103805-2. Retrieved 20 Nov 2013.