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Combining Pattern Classifiers: Methods and Algorithms, 2nd Edition by Ludmila I. Kuncheva

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PREFACE

Pattern recognition is everywhere. It is the technology behind automatically identifying fraudulent bank transactions, giving verbal instructions to your mobile phone, predicting oil deposit odds, or segmenting a brain tumour within a magnetic resonance image.

A decade has passed since the first edition of this book. Combining classifiers, also known as “classifier ensembles,” has flourished into a prolific discipline. Viewed from the top, classifier ensembles reside at the intersection of engineering, computing, and mathematics. Zoomed in, classifier ensembles are fuelled by advances in pattern recognition, machine learning and data mining, among others. An ensemble aggregates the “opinions” of several pattern classifiers in the hope that the new opinion will be better than the individual ones. Vox populi, vox Dei.

The interest in classifier ensembles received a welcome boost due to the high-profile Netflix contest. The world’s research creativeness was challenged using a difficult task and a substantial reward. The problem was to predict whether a person will enjoy a movie based on their past movie preferences. A Grand Prize of $1,000,000 was to be awarded to the team who first achieved a 10% improvement on the classification accuracy of the existing system Cinematch. The contest was launched in October 2006, and the prize was awarded in September 2009. The winning solution was nothing else but a rather fancy classifier ensemble.

What is wrong with the good old single ...

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