By Panos M. Pardalos, Antonio Mucherino, Petraq J. Papajorgji
Data Mining in Agriculture represents a accomplished attempt to supply graduate scholars and researchers with an analytical textual content on info mining strategies utilized to agriculture and environmental similar fields. This e-book offers either theoretical and functional insights with a spotlight on proposing the context of every information mining method particularly intuitively with plentiful concrete examples represented graphically and with algorithms written in MATLAB®.
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Sample text
If particular properties about the model are not known, but high oscillations must be avoided, then a spline function can be used, instead of a polynomial. A spline is a function defined piecewise by polynomials. It is used for avoiding the phenomenon of the increase of oscillations when the degree of a polynomial increases. Indeed, a spline locally is a polynomial having a low degree, so that its oscillations are low. In its general form a polynomial spline S : [a, b] −→ consists of polynomial pieces Pi : [ti , ti+1 ) ∈ [a, b] −→ ∀i ∈ {1, 2, .
The otherwise fraudulent users are instead those that have a sudden change in their calling behavior which generates an abnormal increase of the bill amount. Users having two or more unpaid bills and having a debt less than 10 times their monthly bill are classified as insolvent. Finally, users who paid all the bills or with one unpaid bill only can be classified as normal. 1). The 22 neurons on the input layer correspond to the 22 pieces of information collected from the user during the application.
Chapter 4 focuses on the k-nearest neighbor approach. In this chapter, many strategies for reducing the training sets used in the k-nearest neighbor approach are presented. Chapter 5 is dedicated to artificial neural networks, and hence to the training, pruning and testing process of a neural network. Chapter 6 is on support vector machines. This technique is introduced as a simple linear classifier able to discriminate between two classes only. Then it is extended to the general case when the classes are more than two and they are not linearly separable.