When size matters: selection of training sets for support vector machines Future Processing

The amount of data produced every day grows tremendously in most real-life domains, including medical imaging, genomics, text categorisation, computational biology, and many others. Although it appears beneficial at the first glance (more data could mean more possibilities of extracting and revealing useful underlying knowledge), handling massively large datasets became a challenging issue and attracts research attention, especially in the era of big data. This big data revolution affected many research fields, including statistics, machine learning, parallel computing, and computer systems in general [1]. Storing and analysing the acquired historical information should allow predicting the label of an incoming (unseen) feature vector, containing some quantified features of a given data example. If the labels are categorical, then we are to tackle the classification task (it's regression otherwise).