Extreme Learning Machines 2013: Algorithms and Applications
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
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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