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 ...
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...
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 ...
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...
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 e
5. The model implementation is based on Python and leverages relevant machine learning libraries, most notably the GPy library. GPy allows for the flexible construction and optimization of Gaussian process models. In the previous section on Gaussian kernel function optimization, the module called by ...
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 ...
Additionally, a machine learning (ML)-based GPU sharing mechanism is presented to select pairs of kernels in OpenCL frameworks. The model first selects suitable architecture for the jobs and then merges GPU kernels for better resource utilization. From all the GPU candidates, the optimal pair of...