Learning rule
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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:
Types
Hebb's Rule
Developed by Donal Hebb in 1949 to describe biological neuron firing. It defines Hebbian Learning with respect to Biological Neurons, which was in the min-1950s also applied to computer simulations of neural networks in Artificial Neural Networks.
Perceptron Learning Rule
The perceptron learning rule originates from the Hebbian assumption, and was used by Frank Rosenblatt in his perceptron. The net is passed to the activation (transfer) function and the function's output is used for adjusting the weights.
Widrow-Hoff Learning (Delta Learning Rule)
Similar to the perceptron learning rule but with different origin. It was developed for use in the ADALAINE network. The weights are adjusted according to the weighted sum of the inputs (the net). This makes it ADALINE different from the normal perceptron. Sometimes when Widrow-Hoff is applied to binary targets specifically, it is referred to as Delta Rule.
Back-propagation[3]
Seppo Linnainmaa in 1970 developed Backpropagation and automatic differentiation. It is a generalisation of the least mean squares algorithm in the linear perceptron
See also
- Machine learning
- Decision tree learning
- Pattern recognition
- Bias-variance dilemma
- Bias of an estimator
References
- ^ 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.
- ^ S Russell, P Norvig (1995). "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.
- ^ Schmidhuber, Juergen (2015-01). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. doi:10.1016/j.neunet.2014.09.003.
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