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Proceedings of ELM-2014 Volume 1 = Algorithms and Theories /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Proceedings of ELM-2014 Volume 1/ edited by Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh.
其他題名:	Algorithms and Theories /
其他作者:	Cao, Jiuwen.
面頁冊數:	VIII, 446 p. 124 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-14063-6
ISBN:	9783319140636

Proceedings of ELM-2014 Volume 1 = Algorithms and Theories /
Proceedings of ELM-2014 Volume 1Algorithms and Theories /[electronic resource] :edited by Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh. - 1st ed. 2015. - VIII, 446 p. 124 illus.online resource. - Proceedings in Adaptation, Learning and Optimization,32363-6084 ;. - Proceedings in Adaptation, Learning and Optimization,1.

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. .

ISBN: 9783319140636

Standard No.: 10.1007/978-3-319-14063-6doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Proceedings of ELM-2014 Volume 1 = Algorithms and Theories /
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