000 | 03379nam a22005415i 4500 | ||
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001 | 978-3-031-30609-9 | ||
003 | DE-He213 | ||
005 | 20240207153606.0 | ||
007 | cr nn 008mamaa | ||
008 | 230529s2023 sz | s |||| 0|eng d | ||
020 |
_a9783031306099 _9978-3-031-30609-9 |
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_aUYQ _2thema |
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_a006.3 _223 |
100 | 1 |
_aRehbach, Frederik. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aEnhancing Surrogate-Based Optimization Through Parallelization _h[electronic resource] / _cby Frederik Rehbach. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2023. |
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300 |
_aX, 115 p. 33 illus., 26 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v1099 |
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500 | _aAcceso multiusuario | ||
505 | 0 | _aIntroduction -- Background -- Methods/Contributions -- Application -- Final Evaluation. | |
520 | _aThis book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case. Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently. | ||
541 |
_fUABC ; _cPerpetuidad |
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650 | 0 | _aComputational intelligence. | |
650 | 0 |
_aEngineering _xData processing. |
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650 | 1 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aData Engineering. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031306082 |
776 | 0 | 8 |
_iPrinted edition: _z9783031306105 |
776 | 0 | 8 |
_iPrinted edition: _z9783031306112 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v1099 |
|
856 | 4 | 0 |
_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-30609-9 |
912 | _aZDB-2-INR | ||
912 | _aZDB-2-SXIT | ||
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