In classification problems, the task is to model the decision boundary between a set of distributions in the feature space . This decision boundary is a surface of dimension N-1, where N is the number of relevant features/inputs.
The conventional ANN algorithms for classification problems are the MLP and Learning Vector Quantization ( LVQ) . The MLP needs hidden units to create the decision surface, whereas a nearest neighbour approach, like LVQ, needs units . Hence, the MLP is in general more parsimonious in parameters than nearest neighbour approaches for pattern classification. In special cases, when the decision surface is highly disconnected, the LVQ approach may work better. We have found the MLP to work better than LVQ for all HEP problems encountered so far.
Approaches that combine the advantages of MLP and LVQ  seem to work better than just using an MLP (see below on modular architectures).
Some MLP-like approaches with skip-layer connections and iterative construction algorithms, like the Cascade Correlation algorithm , can construct very complex decision boundaries with a small number of hidden units. It is, however, uncertain how sensitive they are to overtraining.
Function fitting and prediction
In a function fitting problem, the task is to model a real-valued target function f from a number of (noisy) examples.
The straightforward ANN approach is to use the MLP with appropriate number of layers and units [41,43]. Another is the ``local map'' where a partitioning algorithm, like k-means clustering , is used to divide the feature space into subregions. Each subregion is then associated with a function -- a local map [42,45,46]. This method is similar in spirit to statistical methods like regression trees and splines [47,48]. Both the MLP and the local map approaches work well and which method to choose depends on how local the problem is.
A third approach, which is often suggested for time-series prediction, is to use recurrent networks with feed-back connections. However, in our experience with time series the simple MLP produces as good solutions as recurrent networks, within much shorter training times, given that one is using the appropriate time lagged inputs .