The original kernel extreme learning machine (KELM) employs all training samples to construct hidden layer, thus avoiding the performance fluctuations caused by the ELM randomly assigning weights. However, excessive nodes will inevitably lead to structural redundancy, which hinders its application in ...
In the online phase OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) is applied to update the initial model and adapt the recognition model to new device users based on recognition results with high confidence level efficiently. Experimental results show that, the proposed model ...
The MRKELM model is developed on the basis of the multiple kernel leaning method and the reduced kernel extreme learning machine method. In the presented MRKELM, the kernel function are not fixed anymore, multiple kernels are adaptively trained as a hybrid kernel and the optimal kernel ...
Yang and Lin (2017) proposed an approach combining empirical mode decomposition (EMD) and phase space reconstruction (PSR) with extreme learning machine (ELM). In this approach, first, the input series was decomposed into one residual component and several components of intrinsic mode function (IMF...
The advent of computing power, leading to increasingly deep machine learning architectures, has rendered such methods extremely capable of recreating even complex dynamics9,10. However, such methods always remain limited by the breadth and quality of the data used to train them11,12. Physics-based...
Through kernel density estimation, the distribution of DEM errors in different slope ranges can be visually observed (Fig.11d,e). HQTP30 displayed a more concentrated characteristic across all slope ranges, which indicates that HQTP30 not only possesses lower mean errors but also exhibits more min...
Reduced Kernel Extreme Learning Machine. W Deng,Q Zheng,K Zhang. . 2013W. Deng, Q. Zheng, K. Zhang, "Reduced kernel extreme learning machine", in: Proc. 8th Int. Conf. Comput. Recognit. Syst. CORES 2013, 2013: pp. 63-69.
Reduced Kernel Extreme Learning Machine for Traffic Sign Recognitiondoi:10.1109/itsc.2019.8917394E. Sanz-MadozJ. EchanobeO. Mata-CarballeiraI. del CampoM. V. MartinezIEEEInternational Conference on Intelligent Transportation Systems
Kernel methodSupport vector machineRBF networkIn this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which ...
This reduction in the number of kernels will lead to reduced kernel matrix of size,N脳Nleading to decrease in the computational complexity. This work creates a number of balanced kernel subsets depending on the degree of class imbalance. A number of RKWELM based classification models are ...