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Scaling Up Generalized Kernel Methods.


Kernel Approximation Methods for Speech Recognition


Predicting Reaction Yields via Supervised Learning.



Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond



Evolutionary Overview and Prediction of Themes in the Field of Land Degradation



Parameter extraction with reservoir computing: Nonlinear time series analysis and application to industrial maintenance.



Oscillons, walking droplets, and skipping stones (an overview)



Graph-Adaptive Incremental Learning Using an Ensemble of Gaussian Process Experts



Amoebic Foraging Model of Metastatic Cancer Cells



Myths and mechanisms: RecBCD and Chi hotspots as determinants of self vs. non-self



Random Features Strengthen Graph Neural Networks



Online Unsupervised Learning Using Ensemble Gaussian Processes with Random Features



ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features



Investigation of Event-Based Surfaces for High-Speed Detection, Unsupervised Feature Extraction, and Object Recognition



Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization



Subjective-Probability Approach to Design an Expert System for Assessment of States of complex Systems in Conditions of Non-Regular Destructive Influences



Fault Diagnosis of Railway Turnout Based on Random Forests



Random Features and Random Neurons for Brain-Inspired Big Data Analytics



MRC4: A Modified RC4 Algorithm Using Symmetric Random Function Generator for Improved Cryptographic Features



Variational and Hierarchical Recurrent Autoencoder



Scalable Semi-Supervised SVM via Triply Stochastic Gradients



Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images



An adaptive dynamically low-dimensional approximation method for multiscale stochastic diffusion equations



A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency



The Photovoltaic Output Prediction Based on Variational Mode Decomposition and Maximum Relevance Minimum Redundancy


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