Scaling up machine learning: parallel and distributed approaches
This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous datase...
Gespeichert in:
| Weitere Verfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2012
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students and practitioners |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xvi, 475 pages) |
| ISBN: | 9781139042918 |
| DOI: | 10.1017/CBO9781139042918 |
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| 520 | |a This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students and practitioners | ||
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| discipline | Informatik |
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| id | DE-604.BV043942615 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:18Z |
| institution | BVB |
| isbn | 9781139042918 |
| language | English |
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| spelling | Scaling up machine learning parallel and distributed approaches edited by Ron Bekkerman, Mikhail Bilenko, John Langford Cambridge Cambridge University Press 2012 1 online resource (xvi, 475 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students and practitioners Machine learning Data mining Parallel algorithms Parallel programs (Computer programs) Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s 1\p DE-604 Bekkerman, Ron edt Bilenko, Mikhail 1978- edt Langford, John 1975- edt Erscheint auch als Druckausgabe 978-0-521-19224-8 https://doi.org/10.1017/CBO9781139042918 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Scaling up machine learning parallel and distributed approaches Machine learning Data mining Parallel algorithms Parallel programs (Computer programs) Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Scaling up machine learning parallel and distributed approaches |
| title_auth | Scaling up machine learning parallel and distributed approaches |
| title_exact_search | Scaling up machine learning parallel and distributed approaches |
| title_full | Scaling up machine learning parallel and distributed approaches edited by Ron Bekkerman, Mikhail Bilenko, John Langford |
| title_fullStr | Scaling up machine learning parallel and distributed approaches edited by Ron Bekkerman, Mikhail Bilenko, John Langford |
| title_full_unstemmed | Scaling up machine learning parallel and distributed approaches edited by Ron Bekkerman, Mikhail Bilenko, John Langford |
| title_short | Scaling up machine learning |
| title_sort | scaling up machine learning parallel and distributed approaches |
| title_sub | parallel and distributed approaches |
| topic | Machine learning Data mining Parallel algorithms Parallel programs (Computer programs) Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Data mining Parallel algorithms Parallel programs (Computer programs) Maschinelles Lernen |
| url | https://doi.org/10.1017/CBO9781139042918 |
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