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 16113 FWCI3333.6766

Ensemble deep learning: A review

review Full Text OpenAlex 1980 FWCI249.5949

Particle Swarm Optimization: A Comprehensive Survey

article Full Text OpenAlex 1233 FWCI154.2881

Activation functions in deep learning: A comprehensive survey and benchmark

article Full Text OpenAlex 1054 FWCI96.9333

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

article Full Text OpenAlex 782 FWCI65.3905

Feature dimensionality reduction: a review

review Full Text OpenAlex 708 FWCI66.224

An Overview of Variants and Advancements of PSO Algorithm

article Full Text OpenAlex 637 FWCI79.9909

Deep Long-Tailed Learning: A Survey

article Full Text OpenAlex 556 FWCI90.3873

Transfer learning: a friendly introduction

article Full Text OpenAlex 509 FWCI60.8097

A modified Adam algorithm for deep neural network optimization

article Full Text OpenAlex 456 FWCI52.2417