MARC details
| 000 -LEADER |
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| fixed length control field |
01905nmm a2200205Ia 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220920s9999||||xx |||||||||||||| ||und|| |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781139042918 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
B398S |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bekkerman, R. |
| Relator term |
Author |
| Language of a work |
English |
| 9 (RLIN) |
1796 |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
Scaling Up Machine Learning |
| Remainder of title |
: Parallel and Distributed Approaches |
| Statement of responsibility, etc. |
/ edited by R. Bekkerman and others. |
| Medium |
[Electronic Resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Cambridge |
| Name of publisher, distributor, etc. |
: Cambridge University Press, |
| Date of publication, distribution, etc. |
2011 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 475p. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer Science |
| 9 (RLIN) |
926 |
|
|---|
| Topical term or geographic name entry element |
Pattern Recognition And Machine Learning |
| 9 (RLIN) |
1797 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bilenko, M. |
| Relationship information |
[Author] |
| 9 (RLIN) |
1798 |
|
|---|
| Personal name |
Langford, J. |
| Relationship information |
[Editor] |
| 9 (RLIN) |
1799 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://doi.org/10.1017/CBO9781139042918">https://doi.org/10.1017/CBO9781139042918</a> |
| Electronic format type |
PDF |
| Link text |
Click to Access the Online Book |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
e-Book |
| Suppress in OPAC |
|
