State of the art review: the data revolution in critical care

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2015 and co-published as a series in Critical Care. Other articles in the series can be found online at http://ccforum.com/series/annualupdate2015. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901

In The MIT Encyclopedia of the Cognitive Sciences (1999)

A feed-forward network can be viewed as a graphical representation of parametric function which takes a set of input values and maps them to a corresponding set of output values (Bishop, 1995). Figure 1 shows an example of a feed-forward network of a kind that is widely used in practical applications. Nodes in the bias z0 bias y 1 z 1 outputs y c x0 x1 xd inputs hidden units Figure 1: A feed-forward network having two layers of adaptive parameters. graph represent either inputs, outputs or ‘hidden ’ variables, while the edges of the graph correspond to the adaptive parameters. We can write down the analytic function corresponding to this network follows