Sentence examples for of extreme learning from inspiring English sources
However, it is difficult to enhance the modeling ability of extreme learning in disposing the high-dimensional noisy data.
To compact the architecture of extreme learning machine (ELM), two incremental learning algorithms are proposed in this paper.
In particular, the considered deep architecture has been designed under the concept of extreme learning machine (ELM).
In this paper, we propose a silicon implementation of extreme learning machines (ELM) using spiking neural circuits.
After two years of extreme learning - including countless broken limbs and ligaments, but also five second-tier race victories - she would argue otherwise.
The rigorous proof of universal approximation of extreme learning machine with much milder conditions makes it a preferable choice in many different approaches.
The model was based on extreme learning machine (ELM) combined with a novel representation of protein sequence descriptor.
Based on this idea, the objective function of regularized extreme learning machine (RELM) can be written as, begin{array}{*{20}l} & mathbf{Minimize :} parallel {mathbf{H} boldsymbol{beta} - mathbf{T} }{parallel^{2}_{2}} + frac {C}{2} parallel boldsymbol{beta} {parallel^{2}_{2}} end{array} (9).
For this purpose, a construct of an extreme learning machine (ELM) is used as a global model, and this is compensated through a conditional fuzzy C-means (CFCM -based fuzzy inferenCFCM -basedfuzzywinferenceagi–system–Kang (TSK)-type method, which captures the remaining localized nonlinearities oFIShe model.
This study presents the Colour Feature Extreme Learning Machine (CF-ELM), which is an implementation of the extreme learning machine (ELM), with a partially connected hidden layer and a fully connected output layer, taking three colour inputs instead of the standard grey-scale input.
In this investigation, the authors test the efficacy and reliability of optimally pruned extreme learning machine with ensemble of regularization techniques to identify the exhaust gas temperature (Texh) and the engine-out hydrocarbon emission (HCraw) during the coldstart operation of automotive engines.
