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| 008 | 141204s2015 gw | s |||| 0|eng d | ||
| 020 | _a9783319140636 | ||
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_a10.1007/978-3-319-14063-6 _2doi |
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| 040 |
_bspa _dES-MaUEC |
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| 050 | 4 |
_aTA347.A78 _bP763 2015 EB |
|
| 245 | 1 | 0 |
_aProceedings of ELM-2014 Volume 1 _bAlgorithms and Theories _cedited by Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh. |
| 264 | 1 |
_aCham _bSpringer International Publishing _c2015 |
|
| 300 | _a1 recurso en línea (VIII, 446 páginas 124 ilustraciones) | ||
| 336 |
_2rdacontent _aTexto (visual) _btxt |
||
| 337 |
_2rdamedia _aelectrónico _bc |
||
| 338 |
_2rdacarrier _arecurso electrónico _bcr |
||
| 490 | 0 |
_aProceedings in Adaptation, Learning and Optimization, _x2363-6084 ; _v3 |
|
| 490 | 0 | _aEngineering (Springer-11647) | |
| 505 | 0 | _aSparse Bayesian ELM handling with missing data for multi-class classification -- A Fast Incremental Method Based on Regularized Extreme Learning Machine -- Parallel Ensemble of Online Sequential Extreme Learning Machine Based on MapReduce -- Explicit Computation of Input Weights in Extreme Learning Machines -- Subspace Detection on Concept Drifting Data Stream -- Inductive Bias for Semi-supervised Extreme Learning Machine -- ELM based Efficient Probabilistic Threshold Query on Uncertain Data -- Sample-based Extreme Learning Machine Regression with Absent Data -- Two Stages Query Processing Optimization based on ELM in the Cloud -- Domain Adaption Transfer Extreme Learning Machine -- Quasi-linear extreme learning machine model based nonlinear system identification -- A novel bio-inspired image recognition network with extreme learning machine -- A Deep and Stable Extreme Learning Approach for Classification and Regression -- Extreme Learning Machine Ensemble Classifier for Large-scale Data -- Pruned Extreme Learning Machine Optimization based on RANSAC Multi Model Response Regularization -- Learning ELM network weights using linear discriminant analysis -- An Algorithm for Classification over Uncertain Data based on Extreme Learning Machine -- Training Generalized Feedforward Kernelized Neural Networks on Very Large Datasets for Regression Using Minimal-Enclosing-Ball Approximation -- An Online Multiple Model Approach to Improve Performance in Univariate Time-Series Prediction -- A Self-organizing Mixture Extreme Leaning Machine for Time Series Forecasting -- A Robust AdaBoost.RT based Ensemble Extreme Learning Machine -- Machine learning reveals different brain activities during TOVA test -- Online Sequential Extreme Learning Machine with New Weight-setting Strategy or Non stationary Time Series Prediction -- RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement -- Extreme Learning Machine for Regression and Classification Using L1-Norm and L2-Norm -- A Semi-supervised Online Sequential Extreme Learning Machine Method -- ELM feature mappings learning: Single-hidden-layer feed forward network without output weight -- ROS-ELM: A Robust Online Sequential Extreme Learning Machine for Big Data -- Deep Extreme Learning Machines for Classification -- C-ELM: A Curious Extreme Learning Machine for Classification Problems -- Review of Advances in Neural Networks: Neural Design Technology Stack -- Applying Regularization Least Squares Canonical Correction Analysis in Extreme Learning Machine formulti-label classification problems -- Least Squares Policy Iteration based on Random Vector Basis -- Identifying Indistinguishable Classes in Multi-class Classification Data Sets using ELM -- Effects of Training Datasets on both the Extreme Learning Machine and Support Vector Machine for Target Audience Identification on Twitter -- Extreme Learning Machine for Clustering. | |
| 520 | 3 | _aThis book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of "learning without iterative tuning". The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM. . | |
| 988 | _aEBSPRINGER_2018 | ||
| 650 | 7 |
_aInteligencia artificial _vCongresos y asambleas _2embne _9413115 |
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| 700 |
_aCao, Jiuwen _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _998413 |
||
| 700 | 1 |
_aMao, Kezhi. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _998414 |
|
| 700 | 1 |
_aCambria, Erik _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/no2012155591 _1http://viaf.org/viaf/283846500/ _993992 |
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| 700 | 1 |
_aMan, Zhihong. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/no2004036104 _1http://viaf.org/viaf/14478686/ |
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| 700 | 1 |
_aToh, Kar-Ann. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/nb2012000355 _1http://viaf.org/viaf/230056661/ |
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| 776 | 0 | 8 |
_iEdición impresa: _z9783319140643 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319140629 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319366845 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-14063-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 998 |
_b03/2019 _dz _eIG _zSI |
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