Extreme learning machine Kernel Classifier calibration 1Introduction In the age of big data, there are a huge number of varied data, but manually labeling these data is very difficult and expensive [10,19,34,35]. In order to solve the situation, many machine learning techniques called weak-lab...
Kernel extreme learning machine Huang et al.46 improved the ELM through the utilization of the kernel function (K), which altered the feature mapping g(x) associated with the hidden layer47. Not only does the kernel function \(Krn(x,{x}_{1})\) reduce the number of internal variables in...
3.1. Kernel Extreme Learning Machine KELM is based on ELM with the addition of kernel functions. Kernel functions are used to map input training data into high-dimensional feature spaces, thereby replacing the kernel function operation in the original space with the inner product operation in the ...
Fast kernel extreme learning machine for ordinal regression Given a training set {(xi,ti)}i=1N, where xi=[xi1,xi2,…,xiD]⊤∈RD represents the ith input vector, ti∈T is the corresponding label, D is the number of input features, and T={C1,C2,…,Cq,…,CQ} is the ordered labe...
Now, with the given parameters, we can solve for X and hence for k×(G,G′) in O(n3) runtime using an available Matlab code called “dylap”.2 2. Conjugate gradient. For a matrix M and a vector b we can use Conjugate Gradient (CG) methods to efficiently solve the system of ...
extreme speed on both small and large data sets, Bindings forR,Python,MATLAB / Octave,Java, andSpark, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, ...
To overcome these problems, we propose a mixed-kernel online sequential extreme learning machine (MIXEDKOSELM) with Kalman filter, which corrects the error of Kalman filtering algorithm, thus improving the accuracy of the image-based visual servoing (IBVS) system significantly. ...
To authenticate the performance of the proposed deep learning (DL) technique (KELM-AE), high-frequency data of different financial market like Yes Bank, SBI, ASHR, and DJI are taken into consideration and the performance of the proposed technique is investigated in MATLAB based simulation in ...
Complex power quality disturbances (CPQDs) classification can be regarded as a typical application of multi-label (ML) learning. In this study, we propose a new recognition method for CPQDs based on S-transform (ST) and a hybrid kernel function-based extreme learning machine (ELM) for ML ...
“Kernel incremental extreme learning machine”. The concepts of ICOA and IBSO were explained in detail in sections “Gaussian global best-growing operator” and “Tent mapping inverse learning. The implementation of the proposed hybrid intelligent optimization method was summed up in section “Dynamic...