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  • Produktbild: Proceedings of ELM-2014 Volume 1
  • Produktbild: Proceedings of ELM-2014 Volume 1
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Proceedings of ELM-2014 Volume 1 Algorithms and Theories

Aus der Reihe Proceedings in Adaptation, Learning and Optimization

197,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.08.2016

Abbildungen

VIII, 446 p. 124 illus.

Herausgeber

Jiuwen Cao + weitere

Verlag

Springer

Seitenzahl

446

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

686 g

Auflage

Softcover reprint of the original 1st ed. 2015

Sprache

Englisch

ISBN

978-3-319-36684-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.08.2016

Abbildungen

VIII, 446 p. 124 illus.

Herausgeber

Verlag

Springer

Seitenzahl

446

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

686 g

Auflage

Softcover reprint of the original 1st ed. 2015

Sprache

Englisch

ISBN

978-3-319-36684-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Proceedings of ELM-2014 Volume 1
  • Produktbild: Proceedings of ELM-2014 Volume 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 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.