Ontotext

MARCH 8, 2023

Several factors are driving the adoption of knowledge graphs. Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 57–78. Chawla et al.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Conference on Knowledge Discovery and Data Mining, pp.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

by OMKAR MURALIDHARAN Many machine learning applications have some kind of regression at their core, so understanding large-scale regression systems is important. But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. For more on ad CTR estimation, refer to [2].

KDD 40

KDD Testing Machine Learning Modeling 40