Statistical machine learning: a unified framework
"The recent rapid growth in the variety and complexity of new machine learning architectures require the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning provides students, engineers, and scientis...
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2020
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| Edition: | First edition |
| Series: | Chapman & Hall/CRC texts in statistical science series
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| Subjects: | |
| Summary: | "The recent rapid growth in the variety and complexity of new machine learning architectures require the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms"-- |
| Item Description: | Literaturverzeichnis Seite 473-489 |
| Physical Description: | xviii, 506 Seiten 24 cm |
| ISBN: | 9781138484696 1138484695 9780367494223 0367494221 |
Staff View
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| 505 | 8 | |a A statistical machine learning framework -- Set theory for concept modeling -- Formal machine learning algorithms -- Linear algebra for machine learning -- Matrix calculus for machine learning -- Convergence of time-invariant dynamical systems -- Batch learning algorithm convergence -- Random vectors and random functions -- Stochastic sequences -- Probability models of data generation -- Monte Carlo Markov chain algorithm convergence -- Adaptive learning algorithm convergence -- Statistical learning objective function design -- Simulation methods for evaluating generalization -- Analytic formulas for evaluating generalization -- Model selection and evaluation | |
| 520 | 3 | |a "The recent rapid growth in the variety and complexity of new machine learning architectures require the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms"-- | |
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Record in the Search Index
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| author | Golden, Richard M. |
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| contents | A statistical machine learning framework -- Set theory for concept modeling -- Formal machine learning algorithms -- Linear algebra for machine learning -- Matrix calculus for machine learning -- Convergence of time-invariant dynamical systems -- Batch learning algorithm convergence -- Random vectors and random functions -- Stochastic sequences -- Probability models of data generation -- Monte Carlo Markov chain algorithm convergence -- Adaptive learning algorithm convergence -- Statistical learning objective function design -- Simulation methods for evaluating generalization -- Analytic formulas for evaluating generalization -- Model selection and evaluation |
| ctrlnum | (OCoLC)1179293796 (DE-599)BVBBV046826707 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | First edition |
| format | Book |
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| id | DE-604.BV046826707 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T15:03:50Z |
| indexdate | 2024-07-10T08:54:56Z |
| institution | BVB |
| isbn | 9781138484696 1138484695 9780367494223 0367494221 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032234932 |
| oclc_num | 1179293796 |
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| owner_facet | DE-19 DE-BY-UBM DE-573 |
| physical | xviii, 506 Seiten 24 cm |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | Chapman & Hall/CRC texts in statistical science series |
| spelling | Golden, Richard M. Verfasser (DE-588)1204661987 aut Statistical machine learning a unified framework Richard M. Golden First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2020 xviii, 506 Seiten 24 cm txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC texts in statistical science series Literaturverzeichnis Seite 473-489 A statistical machine learning framework -- Set theory for concept modeling -- Formal machine learning algorithms -- Linear algebra for machine learning -- Matrix calculus for machine learning -- Convergence of time-invariant dynamical systems -- Batch learning algorithm convergence -- Random vectors and random functions -- Stochastic sequences -- Probability models of data generation -- Monte Carlo Markov chain algorithm convergence -- Adaptive learning algorithm convergence -- Statistical learning objective function design -- Simulation methods for evaluating generalization -- Analytic formulas for evaluating generalization -- Model selection and evaluation "The recent rapid growth in the variety and complexity of new machine learning architectures require the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms"-- Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Machine learning / Statistical methods Computer algorithms Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe 978-1-351-05150-7 |
| spellingShingle | Golden, Richard M. Statistical machine learning a unified framework A statistical machine learning framework -- Set theory for concept modeling -- Formal machine learning algorithms -- Linear algebra for machine learning -- Matrix calculus for machine learning -- Convergence of time-invariant dynamical systems -- Batch learning algorithm convergence -- Random vectors and random functions -- Stochastic sequences -- Probability models of data generation -- Monte Carlo Markov chain algorithm convergence -- Adaptive learning algorithm convergence -- Statistical learning objective function design -- Simulation methods for evaluating generalization -- Analytic formulas for evaluating generalization -- Model selection and evaluation Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Statistical machine learning a unified framework |
| title_auth | Statistical machine learning a unified framework |
| title_exact_search | Statistical machine learning a unified framework |
| title_exact_search_txtP | Statistical machine learning a unified framework |
| title_full | Statistical machine learning a unified framework Richard M. Golden |
| title_fullStr | Statistical machine learning a unified framework Richard M. Golden |
| title_full_unstemmed | Statistical machine learning a unified framework Richard M. Golden |
| title_short | Statistical machine learning |
| title_sort | statistical machine learning a unified framework |
| title_sub | a unified framework |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
| work_keys_str_mv | AT goldenrichardm statisticalmachinelearningaunifiedframework |