Multilayer Perceptrons Sample Clauses

Multilayer Perceptrons. Multilayer perceptrons are the most commonly used network models within bioinformatics currently (Xxxxxxxx) due to their combination of flexibility and generalisability. The original mathematical neutrons created by XxXxxxxxx & Xxxxx did not have a system for learning, with the weights of inputs having to be calculated by hand to optimise outcomes. When this became an automated process (perceptron learning rule), with altering weights according to output feedback, this was termed a perceptron. This enabled a single neuron to learn to perform pattern differentiation with an optimal division plane, using simple feedback improving accuracy over time. When these perceptrons were incorporated into layers to allow more network complexity, these were then termed multilayer perceptions. The perceptron learning rule within multi-layered MLPs is affected by the back-propagation algorithm. In the late 1980’s it was mathematically proven that a three-layer MLP could be used to model any continuous multi-dimensional function. Whilst this proof (Universal Approximation Theorum, published simultaneously by Xxxxxxx and Xxxxxx in the same year, 1989) was able to show that only three layers of network were required to produce an accurate outcome, there were no limitations placed on the number of nodes within each layer. When the number of hidden nodes within an MLP are increased, the network is able to become increasingly more flexible. This is of specific benefit when trying to model the outcomes of complex datasets, especially in those with non-linear associations. Unfortunately, the same feature within MLPs that allow them to have flexibility with complex data also leads to their main drawback, that of over-fitting the original data set. MLPs often use sigmoid functions within the hidden nodes to create multiple divisions within a potential plane of data differentiation, and are the most commonly used type of XXX. These sigmoid functions (much like in logistic regression) divide the data into theoretical planes of differentiation, adding more and more planes until the data is significantly classified with the minimum of error.
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