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What is Multilayer Perceptron?
A multilayer perceptron is a kind of feed forward artificial neural network, which is a mathematic model inspired by the biological neural network. The multilayer perceptron can be used for various machine learning tasks such as classification and regression.
The basic component of a multilayer perceptron is the neuron. In a multilayer perceptron, the neurons are aligned in layers and in any two adjacent layers the neurons are connected in pairs with weighted edges. A practical multilayer perceptron consists of at least three layers of neurons, including one input layer, one or more hidden layers, and one output layers.
Here is an example multilayer perceptron with 1 input layer, 1 hidden layer and 1 output layer:
https://docs.google.com/drawings/d/1DCsL5UiT6eqglZDaVS1Ur0uqQyNiXbZDAbDWtiSPWX8/pub?w=813&h=368
How Multilayer Perceptron works?
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How Multilayer Perceptron is trained in Hama?
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How to use Multilayer Perceptron in Hama?
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Two class learning problem
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Example: XOR problem
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Multi class learning problem
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Example:
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Regression problem
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Example: Predict the sunspot activity
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Advanced Topics
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Parameter setting
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Reference
[1] Tom Mitchell. Machine Learning. [McGraw] Hill, 1997.
[2] Stanford Unsupervised Feature Learning and Deep Learning tutorial. http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
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[3] Jiawei Han and Micheline Kamber. Data Mining Concept and Technology. The Morgan Kaufmann Series in Data Management Systems. 2011.
[4] Christopher M. Bishop. Neural Networks and Pattern Recognition. Oxford University Press. 1995.