Książka Scaling up Machine Learning Ron Bekkerman

Scaling up Machine Learning

Parallel and Distributed Approaches

Autor: Ron Bekkerman
Język: Angielski
Oprawa: Twarda
Dostępność: Dostępna u dostawcy
Wysyłamy za 9-15 dni
533.73
This book presents an integrated collection of representative approaches for scaling up machine lear...

Informacje o książce

Język
Angielski
Oprawa
Książka - Twarda
Data wydania
2011
strony
492
EAN
9780521192248
ISBN
0521192242
Enbook ID
04435005
Waga
1078
Wymiary
185 x 256 x 31

Pełny opis

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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