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| 001 | 76772 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230207040238.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 130217s2013 ne | s |||| 0|eng d | ||
| 020 | _a9789400758247 | ||
| 024 | 7 |
_a10.1007/978-94-007-5824-7 _2doi |
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| 040 | _dES-MaUEC | ||
| 050 | 4 |
_aR858 _b.C546 2013 |
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| 100 | 1 |
_aCleophas, Ton J. _0Local _986087 |
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| 245 | 1 | 0 |
_aMachine Learning in Medicine _cby Ton J. Cleophas, Aeilko H. Zwinderman. |
| 260 |
_aDordrecht, Netherlands _bSpringer International Publishing _c2013 |
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| 300 |
_a1 recurso en línea (XV, 265 p.) _b44 ilustraciones |
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| 336 |
_aTexto (visual) _btxt _2rdacontent |
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| 337 |
_aelectrónico _bc _2rdamedia |
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| 338 |
_arecurso electrónico _bcr _2rdacarrier |
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| 505 | 0 | _aPreface -- 1 Introduction to machine learning -- 2 Logistic regression for health profiling -- 3 Optimal scaling: discretization -- 4 Optimal scaling: regularization including ridge, lasso, and elastic net regression -- 5 Partial correlations -- 6 Mixed linear modelling -- 7 Binary partitioning -- 8 Item response modelling -- 9 Time-dependent predictor modelling -- 10 Seasonality assessments -- 11 Non-linear modelling -- 12 Artificial intelligence, multilayer Perceptron modelling -- 13 Artificial intelligence, radial basis function modelling -- 14 Factor analysis -- 15 Hierarchical cluster analysis for unsupervised data -- 16 Partial least squares -- 17 Discriminant analysis for Supervised data -- 18 Canonical regression -- 19 Fuzzy modelling -- 20 Conclusions. Index. Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2820} Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u2802}Â{u282E}. | |
| 520 | _aMachine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods. | ||
| 650 | 7 |
_2embne _9421371 _aInteligencia artificial en medicina |
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| 650 | 7 |
_2embne _9166090 _aAprendizaje automático |
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| 700 | 1 |
_aZwinderman, Aeilko H. _0Local _986088 |
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| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-94-007-5824-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 907 |
_a.b12823909 _b10-10-17 _c01-10-14 |
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| 942 |
_2lcc _cLE |
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| 945 |
_aR858 .C546 2013 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i1155311x _z06-04-17 |
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| 988 | _aEBSPRINGER | ||
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