DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Neural Networks:
Classification and Regression

Neural networks (often called artificial neural networks to distinguish them from the natural kind found in humans) are a data and processing structure inspired by natural neural networks. The basic idea is to connect a collection of simple neurons into a network. Some of these nodes are identified as input nodes while others are output nodes. The input data is always numeric, perhaps requiring some transformation. The numbers are propagated through the nodes of the network, being modified as they go (multiplied by link weights, and combined with other numbers at nodes), until they pop out at the output nodes. As a classification model the variable values are provided to the input nodes and the ``answer'' pops out at the output node.

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