CLASSIFICATION AND PREDICTION - Universität Wien

Peter Brezany Institut für Softwarewissenschaft, WS 2002 10
Formal Introduction
Neuronal network is a set of connected input/output units where each connection has a
weight associated with it.
Slide 19
During the learning phase, the network learns by adjusting the weights so as to be able to
predict the correct class label of the input samples. Neural network learning is also referred
to as connectionist learning due to the connections between units.
Neuronal networks involve long training times and are therefore more suitable for
applications where this is feasible. They require a number of parameters that are typically
best determined empirically, such as the network topology.
Advantages of neural networks include their high tolerance to noisy data as well as their
ability to classify patterns on which they have not been trained.
The most popular network algorithm is the backpropagation algorithm which performs
learning on a multilayer feed-forward neural network.
A Multilayer Feed-Forward Neural Network
An example of such network is in Fig. 1.
The inputs correspond to the attributes measured for each training sample. The inputs are
fed simultaneously into a layer of units making up the input layer.
Slide 20
The weighted outputs of the units of the input layer are fed simultaneously to a second layer
of “neuronlike” units, known as a hidden layer.
The hidden layer’s weighted outputs can be input to another hidden layer, and so on. The
number of hidden layers is arbitrary, although in practice, usually only one is used.
The weighted outputs of the last hidden layer are input to units making up to output layer,
which emits the networks prediction for given samples.
The units in the hidden layers and output layer are sometimes referred to as neurodes due to
their symbolic basis, or as output units. The network shown in Fig. 1 is a two-layer neural
network. Similarly, a network containing 2 hidden layers is called a three-layer neural
network, and so on.