000 | 03379nam a2200577Ii 4500 | ||
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001 | 9781315151274 | ||
003 | FlBoTFG | ||
005 | 20220711212818.0 | ||
006 | m o d | ||
007 | cr | ||
008 | 190122t20182019fluab ob 001 0 eng d | ||
020 | _a9781315151274(e-book : PDF) | ||
035 | _a(OCoLC)1079363767 | ||
040 |
_aFlBoTFG _cFlBoTFG _erda |
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050 | 4 |
_aQA76.9 _b.B45 |
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072 | 7 |
_aBUS _x061000 _2bisacsh |
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072 | 7 |
_aCOM _x021030 _2bisacsh |
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072 | 7 |
_aCOM _x037000 _2bisacsh |
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072 | 7 |
_aUN _2bicscc |
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082 | 0 | 4 |
_a5.7 _223 |
100 | 1 |
_aMohanty, Soumya , _eauthor. _919847 |
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245 | 1 | 0 |
_aSwarm Intelligence Methods for Statistical Regression / _cby Soumya Mohanty. |
250 | _aFirst edition. | ||
264 | 1 |
_aBoca Raton, FL : _bChapman and Hall/CRC, _c[2018]. |
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264 | 4 | _c©2019. | |
300 |
_a1 online resource (136 pages) : _b19 illustrations, text file, PDF |
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336 |
_atext _2rdacontent |
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337 |
_acomputer _2rdamedia |
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338 |
_aonline resource _2rdacarrier |
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504 | _aIncludes bibliographical references and index. | ||
505 | 0 | 0 | _tChapter 1 Introduction Chapter 2 Stochastic Optimization Theory Chapter 3 Evolutionary Computation and Swarm Intelligence Chapter 4 Particle Swarm Optimization Chapter 5 PSO Applications Appendix A Probability Theory Appendix B Splines Appendix C Analytical minimization. |
520 | 3 | _aA core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges. | |
530 | _aAlso available in print format. | ||
650 | 7 |
_aCOMPUTERS / Database Management / Data Mining. _2bisacsh _912290 |
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650 | 7 |
_aCOMPUTERS / Machine Theory. _2bisacsh _919848 |
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650 | 7 |
_adata analysis. _2bisacsh _93163 |
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650 | 7 |
_agenetic algorithms. _2bisacsh _93938 |
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650 | 7 |
_ahigh-dimensional data. _2bisacsh _919849 |
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650 | 7 |
_amulti-agent systems. _2bisacsh _919850 |
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650 | 7 |
_aoptimization. _2bisacsh _919851 |
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650 | 7 |
_aparametic regression. _2bisacsh _919852 |
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650 | 0 |
_aBig data. _94174 |
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650 | 0 |
_aSwarm intelligence. _96631 |
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650 | 0 |
_aComputational intelligence. _97716 |
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650 | 0 |
_aRegression analysis. _92504 |
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655 | 0 |
_aElectronic books. _93294 |
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710 | 2 |
_aTaylor and Francis. _910719 |
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776 | 0 | 8 |
_iPrint version: _z9781138558182 |
856 | 4 | 0 |
_uhttps://www.taylorfrancis.com/books/9781315151274 _zClick here to view |
942 | _cEBK | ||
999 |
_c72206 _d72206 |