Vimarsana
Biggest News Aggregation in the World

Outlier Detection News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Stay updated with breaking news from Outlier Detection. Get real-time updates on events, politics, business, and more. Visit us for reliable news and exclusive interviews.

Top News In Outlier Detection Today - Breaking & Trending Today

Benefits of a Unified CNAPP and XDR Platform - Vimarsana News

Benefits of a Unified CNAPP and XDR Platform

In this episode of the "Cybersecurity Insights" podcast, Uptycs CEO Ganesh Pai discusses unifying XDR and CNAPP to improve visibility and explains the

Use ChatGPT Code Interpreter for data analysis and more - Vimarsana News

Use ChatGPT Code Interpreter for data analysis and more

This guide provides more information on how you can use ChatGPT Code Interpreter for data analysis by uploading multiple files to analyze

"Deep One-Class Hate Speech Detection Model" by Saugata Bose and Guoxin Su - Vimarsana News

"Deep One-Class Hate Speech Detection Model" by Saugata Bose and Guoxin Su

Hate speech detection for social media posts is considered as a binary classification problem in existing approaches, largely neglecting distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”. As these sentimental types constitute a significant major portion of data, the classification performance is compromised. Moreover, those classifiers often do not generalize well across different datasets due to a relatively small number of hate-class samples. In this paper, we adopt a one-class perspective for hate speech detection, where the detectio...

Source: uow.edu.au
"Bayesian analysis of De distributions in optical dating: Towards a rob" by Bo Li, Zenobia Jacobs et al. - Vimarsana News

"Bayesian analysis of De distributions in optical dating: Towards a rob" by Bo Li, Zenobia Jacobs et al.

In optical dating, especially single-grain dating, various patterns of distributions in equivalent dose (De) are usually observed and analysed using different statistical models. None of these methods, however, is designed to deal with outliers that do not form part of the population of grains associated with the event of interest (the ‘target population’), despite outliers being commonly present in single-grain De distributions. In this paper, we present a Bayesian method for detecting De outliers and making allowance for them when estimating the De value of the target population. We test...

Source: uow.edu.au