Appropriate Problems for Artificial Neural Networks for Artificial Neural Networks in Machine Learning – 17CS73
Most appropriate for problems where,
Instances have many attribute-value pairs: The target function to be learned is defined over instances that can be described by a vector of predefined features.
Target function output may be discrete-valued, real-valued, or a vector of several real- or discrete-valued attributes
Training examples may contain errors: ANN learning methods are quite robust to noise in the training data.
Long training times are acceptable: Network training algorithms typically require longer training times than, say, decision tree learning algorithms. Training times can range from a few seconds to many hours, depending on factors such as the number of weights in the network, the number of training examples considered, and the settings of various learning algorithm parameters.
Fast evaluation of the learned target function may be required. Although ANN learning times are relatively long, evaluating the learned network, in order to apply it to a subsequent instance, is typically very fast.
The ability for humans to understand the learned target function is not important. The weights learned by neural networks are often difficult for humans to interpret. Learned neural networks are less easily communicated to humans than learned rules
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