Simulation of Scour Pattern Around Cross-Vane Structures Using Outlier Robust Extreme Learning Machine

A Robust Online Sequential Extreme Learning Machine

A Robust and Regularized Extreme Learning Machine

In a moment when the study of outlier robustness within Extreme Learning Machine is still in its infancy, we propose a method that combines maximization of the hidden layer’s information transmission, through Batch Intrinsic Plasticity (BIP), with robust estimation of the output weights. This method named R-ELM/BIP generates a reliable solution in the presence of corrupted data with a good gene...

Vehicle detection in driving simulation using extreme learning machine

Automatically driving based on computer vision has attracted more and more attentions from both research and industrial fields. It has two main challenges, high road and vehicle detection accuracy and real-time performance. To study the two problems, we developed a driving simulation platform in a virtual scene. In this paper, as the first step of final solution, the Extreme Learning Machine (E...

Outlier Detection Using Extreme Learning Machines Based on Quantum Fuzzy C-Means

One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structur...

Outlier Robust Online Learning

We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approach. ORL is simple to implement and has provable robustness guarantee—in stark ...

Modeling Discharge Coefficient of Side Weir on Converging Channel Using Extreme Learning Machine

In this study, the discharge coefficient of side weirs located on converging channels was simulated for the first time using a new method of Extreme Learning Machine (ELM). To examine the accuracy of the numerical model, the Monte Carlo simulations were used and the experimental values validation was conducted by the k-fold cross validation method. Then, the input parameters were detected for s...