Proceedings of ELM-2014 Volume 1 (eBook)

Algorithms and Theories

Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh (Herausgeber)

eBook Download: PDF

2014
446 Seiten
Springer International Publishing (Verlag)
978-3-319-14063-6 (ISBN)

Infos

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.

Inhalt

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.

Produkt-Details

• Erscheint lt. Verlag: 4.12.2014
• Reihe/Serie: Proceedings in Adaptation, Learning and Optimization
• Reihe/Serie: Proceedings in Adaptation, Learning and Optimization
• Zusatzinfo: VIII, 446 p. 124 illus.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Technik
• Schlagworte: Big Data Analytics and Machine Learning • ELM 2014 • extreme learning machines • Intelligent Systems • Multiagent Systems • The International Conference on Extreme Learning Machines
• ISBN-10: 3-319-14063-9 / 3319140639
• ISBN-13: 978-3-319-14063-6 / 9783319140636