In this survey we present some of the most used data mining techniques in the field of
agriculture. Some of these techniques, such as the k-means, the k nearest neighbor, artificial …

The nearest neighbor classifier is one of the most used and well-known techniques for
performing recognition tasks. It has also demonstrated itself to be one of the most useful …

Several real-world applications require real-time prediction on resource-scarce devices
such as an Internet of Things (IoT) sensor. Such applications demand prediction models with …

Sparse grids allow one to employ grid-based discretization methods in data-driven
problems. We present an extension of the classical sparse grid approach that allows us to …

Observing the environment and recognising patterns for the purpose of decision making is
fundamental to human nature. This book deals with the scientific discipline that enables …

Combination (ensembles) of classifiers is now a well established research line. It has been
observed that the predictive accuracy of a combination of independent classifiers excels that …

Data Mining in Agriculture represents a comprehensive effort to provide graduate students
and researchers with an analytical text on data mining techniques applied to agriculture and …

Support vector machine is a classification model which has been widely used in many
nonlinear and high dimensional pattern recognition problems. However, it is inefficient or …

This book is concerned with a fundamentally novel approach to graph-based pattern
recognition based on vector space embedding of graphs. It aims at condensing the high …

We present a novel algorithm for computing a training set consistent subset for the nearest
neighbor decision rule. The algorithm, called FCNN rule, has some desirable properties …