English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Density estimation, which estimates the distribution of data, is an importantcategory of probabilistic machine learning. A family of density estimators ismixture models, such as Gaussian Mixture Model (GMM) by expectationmaximization. Another family of density estimators is the generative modelswhich generate data from input latent variables. One of the generative modelsis the Masked Autoregressive Flow (MAF) which makes use of normalizing flowsand autoregressive networks. In this paper, we use the density estimators forclassification, although they are often used for estimating the distribution ofdata. We model the likelihood of classes of data by density estimation,specifically using GMM and MAF. The proposed classifiers outperform simplerclassifiers such as linear discriminant analysis which model the likelihoodusing only a single Gaussian distribution. This work opens the research doorfor proposing other probabilistic classifiers based on joint densityestimation.

Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive Flow