A feedforward network can be viewed as a graphical representation of a 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 feedforward network of a kind that is widely used in practical applications.

Vertices in the 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 as follows. The output of the `j`th hidden node
is obtained by first forming a weighted linear combination of the
`d` input values `x`_{i} to give

The value of hidden variable `j` is then obtained by
transforming the linear sum in (1) using an activation function
`g`(·) to give

`z`_{j}
= `g`(`a`_{j})

Finally, the outputs of the network are obtained by forming linear combinations of the hidden variables to give

The parameters {`u`_{ji},
`v`_{kj}} are called
*weights* while {`b`_{j},
`c` _{k}} are called *biases*, and
together they constitute the adaptive parameters in the network. There
is a one-to-one correspondence between the variables and parameters in
the analytic function and the nodes and edges respectively in the
graph.

Historically, feedforward networks were introduced as models of
biological neural networks (McCulloch and Pitts 1943), in which nodes
corresponded to neurons and edges corresponded to synapses, and with
an activation function `g`(`a`) given by a simple
threshold. The recent development of feedforward networks for pattern
recognition applications has, however, proceeded largely independently
of any biological modeling considerations.

The goal in pattern recognition is to use a set of example solutions to some problem to infer an underlying regularity which can subsequently be used to solve new instances of the problem. Examples include handwritten digit recognition, medical image screening, and fingerprint identification. In the case of feedforward networks, the set of example solutions (called a training set) comprises instances of input values together with corresponding desired output values. The training set is used to define an error function in terms of the discrepancy between the predictions of the network for given inputs and the desired values of the outputs given by the training set. A common example of an error function would be the squared difference between desired and actual output, summed over all outputs and summed over all patterns in the training set. The learning process then involves adjusting the values of the parameters to minimize the value of the error function. Once the network has been trained, that is, once suitable values for the parameters have been determined, new inputs can be applied and the corresponding predictions (i.e., network outputs) calculated.

The use of layered feedforward networks for pattern recognition was widely studied in the 1960s. However, effective learning algorithms were only known for the case of networks in which, at most, one of the layers comprised adaptive interconnections. Such networks were known variously as perceptrons (Rosenblatt 1962) and adalines (Widrow and Lehr 1990), and were seriously limited in their capabilities (Minsky and Papert 1969/1990). Research into artificial NEURAL NETWORKS was stimulated during the 1980s by the development of new algorithms capable of training networks with more than one layer of adaptive parameters (Rumelhart, Hinton, and Williams 1986). A key development involved the replacement of the nondifferentiable threshold activation function by a differentiable nonlinearity, which allows gradient-based optimization algorithms to be applied to the minimization of the error function. The second key step was to note that the derivatives could be calculated in a computationally efficient manner using a technique called backpropagation, so called because it has a graphical interpretation in terms of a propagation of error signals from the output nodes backward through the network. Originally these gradients were used in simple steepest-descent algorithms to minimize the error function. More recently, however, this has given way to the use of more sophisticated algorithms, such as conjugate gradients, borrowed from the field of nonlinear optimization (Gill, Murray, and Wright 1981).

During the late 1980s and early 1990s,
research into feedforward networks emphasized their role as function
approximators. For example, it was shown that a network consisting
of two layers of adaptive parameters could approximate any continuous function
from the inputs to the outputs with arbitrary accuracy provided
the number of hidden units is sufficiently large and provided the
network parameters are set appropriately (Hornik, Stinchcombe, and White
1989). More recently, however, feedforward networks have been studied from
the much richer probabilistic perspective (see PROBABILITY, FOUNDATIONS OF), which sets neural networks firmly within
the field of *statistical pattern recognition* (Fukunaga 1990).
For instance, the outputs of the network can be given a probabilistic
interpretation, and the role of network training is then to model
the probability distribution of the target data, conditioned on
the input variables. Similarly, the minimization of an error function
can be motivated from the well-established principle of maximum
likelihood that is widely used in statistics. An important advantage
of this probabilistic viewpoint is that it provides a theoretical
foundation for the study and application of feedforward networks
(see STATISTICAL LEARNING THEORY), as well
as motivating the development of new models and new learning algorithms.

A central issue in any pattern recognition
application is that of generalization, in other words the performance
of the trained model when applied to previously unseen data. It
should be emphasized that a small value of the error function for
the training data set does not guarantee that future predictions
will be similarly accurate. For example, a large network with many parameters
may be capable of achieving a small error on the training set, and
yet fail to model the underlying distribution of the data and hence
achieve poor performance on new data (a phenomenon sometimes called "overfitting").
This problem can be approached by limiting the complexity of the model,
thereby forcing it to extract regularities in the data rather than
simply memorizing the training set. From a fully probabilistic viewpoint,
learning in feedforward networks involves using the network to define
a *prior* distribution over functions, which is converted
to a *posterior* distribution once the training data have
been observed. It can be formalized through the framework of BAYESIAN LEARNING, or equivalently through the MINIMUM DESCRIPTION LENGTH approach (MacKay 1992; Neal 1996).

In practical applications of feedforward networks, attention must be paid to the representation used for the data. For example, it is common to perform some kind of preprocessing on the raw input data (perhaps in the form of "feature extraction") before they are used as inputs to the network. Often this preprocessing takes into consideration any prior knowledge we might have about the desired properties of the solution. For instance, in the case of digit recognition we know that the identity of the digit should be invariant to the position of the digit within the input image.

Feedforward neural networks are now well established as an important technique for solving pattern recognition problems, and indeed there are already many commercial applications of feedforward neural networks in routine use.

- COGNITIVE MODELING, CONNECTIONIST
- CONNECTIONIST APPROACHES TO LANGUAGE
- MCCULLOCH
- NEURAL NETWORKS
- PITTS
- RECURRENT NETWORKS

- An Analytical Framework for Local Feedforward Networks
- Exact Representations from Feed-Forward Neural Networks
- Structural Optimization of Feedforward Networks

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