专题:Machine Learning and ELM

This cluster of papers focuses on the theory, applications, and advancements in Extreme Learning Machines (ELM), a machine learning framework based on feedforward neural networks with random hidden nodes. The papers cover topics such as incremental learning, classification, regression, ensemble methods, kernel-based models, and their applications in various domains.
最新文献
近5年高被引文献
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

preprint Full Text OpenAlex 16035 FWCI4284.135

Ensemble deep learning: A review

review Full Text OpenAlex 1901 FWCI247.3062

Particle Swarm Optimization: A Comprehensive Survey

article Full Text OpenAlex 1178 FWCI151.9974

Activation functions in deep learning: A comprehensive survey and benchmark

article Full Text OpenAlex 1018 FWCI96.9397

A compute-in-memory chip based on resistive random-access memory

article Full Text OpenAlex 755 FWCI64.8934

Feature dimensionality reduction: a review

review Full Text OpenAlex 680 FWCI66.4613

An Overview of Variants and Advancements of PSO Algorithm

article Full Text OpenAlex 611 FWCI78.4822

Deep Long-Tailed Learning: A Survey

article Full Text OpenAlex 529 FWCI90.9704

Transfer learning: a friendly introduction

article Full Text OpenAlex 472 FWCI60.1369

A modified Adam algorithm for deep neural network optimization

article Full Text OpenAlex 412 FWCI50.1965