000 | 03889cam a2200589Ki 4500 | ||
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001 | 9780429297595 | ||
003 | FlBoTFG | ||
005 | 20220724194335.0 | ||
006 | m o d | ||
007 | cr cnu---unuuu | ||
008 | 201023t20212021flu ob 000 0 eng d | ||
040 |
_aOCoLC-P _beng _erda _epn _cOCoLC-P |
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020 |
_a9780429297595 _qelectronic book |
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_a0429297599 _qelectronic book |
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020 |
_a9781000176810 _qelectronic book |
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_a1000176819 _qelectronic book |
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_a1000176827 _qelectronic book _qMobipocket |
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020 | _z9780367538828 | ||
020 | _z9780367277321 | ||
035 | _a(OCoLC)1201337174 | ||
035 | _a(OCoLC-P)1201337174 | ||
050 | 4 |
_aQ325.75 _b.K65 2021eb |
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072 | 7 |
_aCOM _x037000 _2bisacsh |
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072 | 7 |
_aCOM _x077000 _2bisacsh |
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072 | 7 |
_aPBT _2bicssc |
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082 | 0 | 4 |
_a006.3/1 _223 |
100 | 1 |
_aKolosova, Tanya, _eauthor. |
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245 | 1 | 0 |
_aSupervised machine learning : _boptimization framework and applications with SAS and R / _cTanya Kolosova and Samuel Berestizhevsky. |
250 | _aFirst edition. | ||
264 | 1 |
_aBoca Raton, FL : _bCRC Press, _c2021. |
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264 | 4 | _c©2021 | |
300 | _a1 online resource (xxiv, 160 pages). | ||
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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505 | 0 | _aIntroductionPART 1Introduction to the AI frameworkSupervised Machine Learning and Its Deployment in SAS and RBootstrap methods and Its Deployment in SAS and ROutliers Detection and Its Deployment in SAS and RDesign of Experiment and Its Deployment in SAS and RPART IIIntroduction to the SAS and R based table-driven environmentInput Data componentDesign of Experiment for Machine-Learning component"Contaminated" Training Datasets ComponentPART IIIInsurance Industry: Underwriters decision-making processInsurance Industry: Claims Modeling and PredictionIndex | |
520 | _aAI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn't ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub | ||
588 | _aOCLC-licensed vendor bibliographic record. | ||
650 | 0 | _aSupervised learning (Machine learning) | |
650 | 0 | _aProgram transformation (Computer programming) | |
650 | 0 | _aSAS (Computer program language) | |
650 | 0 | _aR (Computer program language) | |
650 | 7 |
_aCOMPUTERS / Machine Theory _2bisacsh |
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650 | 7 |
_aCOMPUTERS / Mathematical & Statistical Software _2bisacsh |
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700 | 1 |
_aBerestizhevsky, Samuel, _eauthor. |
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856 | 4 | 0 |
_3Read Online _uhttps://www.taylorfrancis.com/books/9780429297595 |
856 | 4 | 2 |
_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
942 |
_2lcc _cEBK |
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999 |
_c16493 _d16493 |