LEADER 05937nam 22006495 450 001 9910299702203321 005 20200703231302.0 010 $a3-319-14063-9 024 7 $a10.1007/978-3-319-14063-6 035 $a(CKB)3710000000311739 035 $a(EBL)1967669 035 $a(OCoLC)897810291 035 $a(SSID)ssj0001408141 035 $a(PQKBManifestationID)11933758 035 $a(PQKBTitleCode)TC0001408141 035 $a(PQKBWorkID)11346403 035 $a(PQKB)10390740 035 $a(DE-He213)978-3-319-14063-6 035 $a(MiAaPQ)EBC1967669 035 $a(PPN)183152026 035 $a(EXLCZ)993710000000311739 100 $a20141204d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProceedings of ELM-2014 Volume 1 $eAlgorithms and Theories /$fedited by Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (446 p.) 225 1 $aProceedings in Adaptation, Learning and Optimization,$x2363-6084 ;$v3 300 $aDescription based upon print version of record. 311 $a3-319-14062-0 320 $aIncludes bibliographical references and index. 327 $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. 330 $aThis book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of ?learning without iterative tuning?.  The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.  . 410 0$aProceedings in Adaptation, Learning and Optimization,$x2363-6084 ;$v3 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3 676 $a620 702 $aCao$b Jiuwen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMao$b Kezhi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCambria$b Erik$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMan$b Zhihong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aToh$b Kar-Ann$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910299702203321 996 $aProceedings of ELM-2014 Volume 1$92530021 997 $aUNINA