Extreme Learning Machines 2013: Algorithms and Applications
Extreme Learning Machines 2013: Algorithms and Applications

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The Extreme Learning Machine classifier is used to perform the perturbative method known as Sensitivity Analysis. The method returns a measure of class sensitivity per attribute. The results show a strong consistency for classifiers with different random input weights. In order to present the results obtained in an intuitive way, two forms of representation are proposed and contrasted against each other. The relevance of both attributes and classes is discussed. Class stability and the ease with which a pattern can be correctly classified are inferred from the results. The method can be used with any classifier that can be replicated with different random seeds

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Stochastic Sensitivity Analysis Using Extreme Learning Machine 1
David Becerra-Alonso, Mariano Carbonero-Ruz,
Alfonso Carlos Martínez-Estudillo
and Francisco José Marténez-Estudillo
Efficient Data Representation Combining with ELM and GNMF 13
Zhiyong Zeng, YunLiang Jiang, Yong Liu and Weicong Liu
Extreme Support Vector Regression 25
Wentao Zhu, Jun Miao and Laiyun Qing
A Modular Prediction Mechanism Based on Sequential
Extreme Learning Machine with Application to Real-Time
Tidal Prediction 35
Jian-Chuan Yin, Guo-Shuai Li and Jiang-Qiang Hu
An Improved Weight Optimization and Cholesky Decomposition
Based Regularized Extreme Learning Machine for Gene
Expression Data Classification 55
ShaSha Wei, HuiJuan Lu, Yi Lu and MingYi Wang
A Stock Decision Support System Based on ELM 67
Chengzhang Zhu, Jianping Yin and Qian Li
Robust Face Detection Using Multi-Block Local Gradient
Patterns and Extreme Learning Machine 81
Sihang Zhou and Jianping Yin
Freshwater Algal Bloom Prediction by Extreme Learning
Machine in Macau Storage Reservoirs 95
Inchio Lou, Zhengchao Xie, Wai Kin Ung and Kai Meng Mok
ELM-Based Adaptive Live Migration Approach
of Virtual Machines 113
Baiyou Qiao, Yang Chen, Hong Wang, Donghai Chen,
Yanning Hua, Han Dong and Guoren Wang
ELM for Retinal Vessel Classification 135
Iñigo Barandiaran, Odei Maiz, Ion Marqués,
Jurgui Ugarte and Manuel Graña
Demographic Attributes Prediction Using Extreme
Learning Machine. 145
Ying Liu, Tengqi Ye, Guoqi Liu, Cathal Gurrin and Bin Zhang
Hyperspectral Image Classification Using Extreme Learning
Machine and Conditional Random Field 167
Yanyan Zhang, Lu Yu, Dong Li and Zhisong Pan
ELM Predicting Trust from Reputation in a Social
Network of Reviewers 179
J. David Nuñez-Gonzalez and Manuel Graña
Indoor Location Estimation Based on Local Magnetic Field
via Hybrid Learning. 189
Yansha Guo, Yiqiang Chen and Junfa Liu
A Novel Scene Based Robust Video Watermarking Scheme
in DWT Domain Using Extreme Learning Machine 209
Charu Agarwal, Anurag Mishra, Arpita Sharma and Girija Chetty

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