05937nam 22006495 450 991029970220332120200703231302.03-319-14063-910.1007/978-3-319-14063-6(CKB)3710000000311739(EBL)1967669(OCoLC)897810291(SSID)ssj0001408141(PQKBManifestationID)11933758(PQKBTitleCode)TC0001408141(PQKBWorkID)11346403(PQKB)10390740(DE-He213)978-3-319-14063-6(MiAaPQ)EBC1967669(PPN)183152026(EXLCZ)99371000000031173920141204d2015 u| 0engur|n|---|||||txtccrProceedings of ELM-2014 Volume 1 Algorithms and Theories /edited by Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (446 p.)Proceedings in Adaptation, Learning and Optimization,2363-6084 ;3Description based upon print version of record.3-319-14062-0 Includes bibliographical references and index.Sparse 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.This 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.  .Proceedings in Adaptation, Learning and Optimization,2363-6084 ;3Computational intelligenceArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.006.3620Cao Jiuwenedthttp://id.loc.gov/vocabulary/relators/edtMao Kezhiedthttp://id.loc.gov/vocabulary/relators/edtCambria Erikedthttp://id.loc.gov/vocabulary/relators/edtMan Zhihongedthttp://id.loc.gov/vocabulary/relators/edtToh Kar-Annedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910299702203321Proceedings of ELM-2014 Volume 12530021UNINA