In this work, it is aimed to aggregate both CKs and MKs autonomously without the need of kernel coefficient adjustment manually. Convex combination of predefined kernel functions is implemented by using multiple kernel extreme learning machine. Thus, complex optimization processes of standard MKL are ...
A prediction method based on multiple kernel extreme learning machine is proposed in this paper to model the complex dynamics of multivariate chaotic time series. First, the multivariate chaotic time series is reconstructed in phase space,transforming the temporal correlation into spatial correlation. ...
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 ...
In order to improve the fault diagnostic accuracy of analog circuit, an improved lp-norm multiple kernel extreme learning machine (ELM) diagnostic model is proposed based on the feature selection algorithm with one-dimensional ambiguity among fault features. In this model, the weighted classification ...
In order to process large image data sets with high feature dimensions, the very efficient algorithm of kernel extreme learning machine is employed to in our study to build image classifiers. In order to avoid the overflow problem, the classification strategy is improved by training classifiers on...
Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine As we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance lea... J Wang,L Cai,X Zhao - 《Journal of Intelligent Systems》...
Classification of eeg signals using a multiple kernel learning support vector machine (Jul 2014) Google Scholar [29] P. Lang, Margaret M. Bradley The international affective picture system (iaps) in the study of emotion and attention Handbook of Emotion Elicitation and Assessment, vol. 29 (2007...
2018) extreme learning machine (ELM)(Bequé & Lessmann, 2017; Yu, Li, Tang, & Gao, 2016), genetic programming (GP) (Abdou, 2009; Ong et al., 2005), case-based reasoning(CBR) (Shin & Han, 2001), and kernel learning technique (Yang, 2007; Zhou et al., 2011) are widely used ...
In order to process large image data sets with high feature dimensions, the very efficient algorithm of kernel extreme learning machine is employed to in our study to build image classifiers. In order to avoid the overflow problem, the classification strategy is improved by training classifiers on...
In the field of tribology, many studies now use machine learning (ML). However, ML models have not yet been used to evaluate the relationship between the friction coefficient and the elemental distribution of a tribofilm formed from multiple lubricant additives. This study proposed the possibility...