Adaptive Many-Core Machines View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Noel Lopes , Bernardete Ribeiro

ABSTRACT

The previous chapters presented a number of novel Machine Learning algorithms and high-performance implementations of existing ones with data scalability in mind. The rationale is to increase their practical applicability to largescale ML problems. The common underlying thread has been the recent progress in usability, cost effectiveness and diversity of parallel computing platforms, specifically, Graphics Processing Units (GPUs), tailored for a broad set of data analysis and machine learning tasks. In this chapter, we provide the main outcomes of the book through a unified view of the rationale behind adaptive many-core machines enlightened by the practical approach taken in this volume. The awareness that big data has sparked large-scale machine learning has put forward a new understanding and thinking into Big Learning. The machine learning community has to take on these challenges by parallelizing the models for the development of successful applications for a class of problems lying on the crossroads of several research topics including data sensing, data mining and data visualization. Thus we include a few promising research directions in large-scale machine learning that are likely to expand in the future. More... »

PAGES

189-200

Book

TITLE

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

ISBN

978-3-319-06937-1
978-3-319-06938-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-06938-8_9

DOI

http://dx.doi.org/10.1007/978-3-319-06938-8_9

DIMENSIONS

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