Guang-Bin Huang, Qin-Yu Zhu and Chee-Kheong Siew, “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks”, IEEE 2004. Google Scholar 2 Akusok, Anton, Kaj-Mikael Björk, Yoan Miche, and Amaury Lendasse, “High-performance extreme learning machines: a complete tool...
Kernel extreme learning machineParameter optimizationFruit fly optimizationBankruptcy predictionSlime mould algorithmLevy flightBankruptcy prediction is a crucial application in financial fields to aid in accurate decision making for business enterprises. Many models may stagnate to low-accuracy results due to ...
and disaster preparedness. Machine learning (ML) techniques are commonly employed for hydrological prediction; however, they still face certain drawbacks, such as the need to optimize the appropriate predictors, the ability of the models to generalize across different time horizons...
Some widely known classifiers will also be included in the comparison: regular perceptron, extreme learning machine regularized via weight decay (ELM-reg), MLP and also the SVM, which has been recently shown notably effective and represents the state-of-art. Besides, the Hebbian perceptron will ...
The kernel trick allows us to employ high-dimensional feature space for a machine learning task without explicitly storing features. Recently, the idea of utilizing quantum systems for computing kernel functions using interference has been demonstrated experimentally. However, the dimension of feature spac...
Li, W., et al.: Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans. Geosci. Rem. Sens. 53(7), 3681–3693 (2015). https://doi.org/10.1109/tgrs.2014.2381602 8. Ghamisi, P., Dalla Mura, M., Benediktsson, J.A.: A survey on spectral...
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, ...
python acnet/acb.py ACNet v2 (Diverse Branch Block, DBB):Diverse Branch Block: Building a Convolution as an Inception-like Unit. DBB (CVPR 2021)is a CNN component with higher performance than ACB and still no inference-time costs. Sometimes I call it ACNet v2 because "DBB" is 2 bits la...
From a machine learning perspective, features extracted from neighboring electrodes should contribute to the prediction process as a single group. For the second question, brain rhythms are highly correlated with cognition [21]. The abovementioned questions could be efficiently addressed by combining ...
With crude oil price as studying sample, the proposed EEMD-based RVFL network performs significantly better in terms of prediction accuracy than not only single algorithms such as RVFL network, extreme learning machine (ELM), kernel ridge regression, random forest, back propagation neural network, ...