000 05272nam a22005175i 4500
001 978-3-319-14063-6
003 DE-He213
005 20200421111649.0
007 cr nn 008mamaa
008 141204s2015 gw | s |||| 0|eng d
020 _a9783319140636
_9978-3-319-14063-6
024 7 _a10.1007/978-3-319-14063-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aProceedings of ELM-2014 Volume 1
_h[electronic resource] :
_bAlgorithms and Theories /
_cedited by Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aVIII, 446 p. 124 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aProceedings in Adaptation, Learning and Optimization,
_x2363-6084 ;
_v3
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 _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.  .
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aCao, Jiuwen.
_eeditor.
700 1 _aMao, Kezhi.
_eeditor.
700 1 _aCambria, Erik.
_eeditor.
700 1 _aMan, Zhihong.
_eeditor.
700 1 _aToh, Kar-Ann.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319140629
830 0 _aProceedings in Adaptation, Learning and Optimization,
_x2363-6084 ;
_v3
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-14063-6
912 _aZDB-2-ENG
942 _cEBK
999 _c54305
_d54305