Data Mining in Agriculture (Springer Optimization and Its by Panos M. Pardalos, Antonio Mucherino, Petraq J. Papajorgji

By Panos M. Pardalos, Antonio Mucherino, Petraq J. Papajorgji

Data Mining in Agriculture represents a complete attempt to supply graduate scholars and researchers with an analytical textual content on facts mining innovations utilized to agriculture and environmental similar fields. This publication offers either theoretical and functional insights with a spotlight on offering the context of every info mining approach really intuitively with abundant concrete examples represented graphically and with algorithms written in MATLAB®.

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The 22 neurons on the input layer correspond to the 22 pieces of information collected from the user during the application. The 2 neurons on the output layer allow the network to distinguish only between two classes: fraudsters and non-fraudsters. The internal layer, the hidden layer, contains 10 neurons. The data obtained from the databases of the telecommunication company and successively classified by an expert are divided in a training set, a validation set and a testing set. In this way, it is possible to control if the network is correctly learning how to classify the data during the training phase using the validation set.

This is a simple optimization problem. 4) to be solved, but it can be solved analytically. The constraint on the coefficient vector α1 can be considered as a penalty term in the objective function: α1T α1 + λ(α1T α1 ), 28 2 Statistical Based Approaches where λ determines the trade-off between constraint satisfaction and maximization of the variance. The derivative with respect to α1 of this function helps locating the function stationary points. The stationary points of the function include their minimum and maximum points.

They may not be present in some formulations, and in that case the optimization problem is unconstrained. There is not only one way for solving these problems, but rather a collection of algorithms, which can be chosen on the basis of the particular needs. Properties of the objective function, or of the constraints, can determine the choice of one algorithm or another. A large variety of optimization methods and algorithms for optimization can be found in [76, 184]. 16 1 Introduction to Data Mining Methods for optimization are mainly divided into deterministic or exact methods and meta-heuristic methods.

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