First is Node2Vec, a popular graph embedding algorithm that uses neural networks to learn continuous feature representations for nodes, which can then be used for downstream machine learning tasks.

SVM and kNN exemplify several important trade-offs in machine learning (ML). SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set ...

This article explores what knowledge graphs are, why they are becoming a favourable data storage format, and discusses their potential to improve artificial intelligence and machine learning ...

In this online data science specialization, you will apply machine learning algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Beginning ...

The graph below shows the total number of publications each year in Instance Selection and Classification Algorithms in Machine Learning. References [1] A hybrid tuple selection pipeline for ...

Combining graphs and machine learning has been getting a lot of attention lately, especially since the work published by researchers from DeepMind, Google Brain, MIT, and the University of Edinburgh.

Graph-decomposed k-NN searching algorithm on road network. Higher Education Press . Journal Frontiers of Computer Science DOI 10.1007/s11704-023-3626-3 ...

Her research focuses on developing innovative algorithms and models that push the boundaries of machine learning, optimization, and artificial intelligence. Subscribe To Newsletters 7:08 ...