Machine Learning for Adaptive Many-Core Machines - A Practical Approach

• Main Authors: Lopes, Noel (Author), Ribeiro, Bernardete (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2015.
• Series: Studies in Big Data, 7
• Subjects: Engineering.; Operations research.; Decision making.; Artificial intelligence.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).; Operation Research/Decision Theory.

 The overwhelming data produced everyday and the increasing performance and cost requirements of applications is transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together. 

 Introduction -- Supervised Learning -- Unsupervised and Semi-supervised Learning -- Large-Scale Machine Learning.