Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in continuous domain take advantage impressive performance generative models for data. Amongst them are recently emerging diffusion probabilistic models, which have observed generating high-quality samples. Recent advances focused on log likeliho...

Aiming at optimal prediction of the correct note corresponding to a vocal time series we trained a classification algorithm on the basis of parts of interpretations of Tochter Zion (Händel) and tested the algorithm on the remaining parts. As classification algorithm we use a radial basis function support vector machine together with a “Hidden Markov” method as a dynamisation mechanism and some ...

this study investigates the relationship between market structure and profitability in the iranian manufacturing industries. in this study, raw data based on international standard industrial classification (at four digit code) has been used. fixed effects panel estimation method (based on diagnostic test results) has been chosen for empirical investigation. the result of the study confirms the...

This study addresses the challenge of assessing carcinogenic potential hazardous chemical mixtures, such as per- and polyfluorinated substances (PFASs), which are known to contribute significantly cancer development. Here, we propose a novel framework called HNNMixCancer that utilizes hybrid neural network (HNN) integrated into machine-learning framework. incorporates mathematical model simulat...

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression such scenarios, including variable screening, model selection, order selection response categories, and selection. perform our gene expression with 801 patients, 2426 genes, five ...

Categorical Attribute traNsformation Environment (CANE) is a simpler but powerful data categorical preprocessing Python package. The package valuable since there currently large range of Machine Learning (ML) algorithms that can only be trained using numerical (e.g., Deep Learning, Support Vector Machines) and several real-world ML applications are associated with attributes. Currently, CANE of...

An identity links the rate of economic growth, the speed of poverty reduction and changes in the distribution of income during some time period in a given country. A few authors used that identity to understand the causes for observed changes in poverty and to identify the exact role of economic growth in poverty reduction. Yet, many empirical cross-country studies of the relationship between g...