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Proceedings of ELM-2014 Volume 1: Algorithms and Theories de Jiuwen Cao

Proceedings of ELM-2014 Volume 1: Algorithms and Theories: Proceedings in Adaptation, Learning and Optimization, cartea 3

Editat de Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh
en Limba Engleză Hardback – 29 dec 2014
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.
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Specificații

ISBN-13: 9783319140629
ISBN-10: 3319140620
Pagini: 456
Ilustrații: VIII, 446 p. 124 illus.
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.81 kg
Ediția:2015
Editura: Springer International Publishing
Colecția Springer
Seria Proceedings in Adaptation, Learning and Optimization

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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 UsingL1-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.

Textul de pe ultima copertă

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.
 

Caracteristici

Recent research on Extreme Learning Machines Results of the International Conference on Extreme Learning Machines (ELM-2014) held at Marina Bay Sands, Singapore, December 8-10, 2014 Presents Theory, Algorithms and Applications