Generalized Poisson Regression Type-II at Jambi City Health Office

One statistical analysis is regression analysis. One regression that has the assumption of poisson distribution is poisson regression which has the assumption of poisson distribution. Neonatal deaths are still very rare, so the proper analysis is used, namely Generalized Poisson Regression. This regression method is specifically used for Poissson distributed data. The stages that will be carried out in this research are Poisson distribution test and equidispersion assumption, parameter estimation, model feasibility test and best model selection. Data from the Jambi City Health Office in 2018 showed that the Generalized Poisson Regression regression alleged had a variable number of first trimester visits, the number of pregnant women getting Tetanus Diptheria immunization, the estimated number of neonatal infants with complications, the number of infants receiving Hepatitis B immunization was less than twenty-four hours, the number of infants receiving BCG immunizations

Similarity-based Classification: Connecting Similarity Learning to Binary Classification

In real-world classification problems, pairwise supervision (i.e., a pair of patterns with a binary label indicating whether they belong to the same class or not) can often be obtained at a lower cost than ordinary class labels. Similarity learning is a general framework to utilize such pairwise supervision to elicit useful representations by inferring the relationship between two data points, which encompasses various important preprocessing tasks such as metric learning, kernel learning, graph embedding, and contrastive representation learning. Although elicited representations are expected to perform well in downstream tasks such as classification, little theoretical insight has been given in the literature so far. In this paper, we reveal that a specific formulation of similarity learning is strongly related to the objective of binary classification, which spurs us to learn a binary classifier without ordinary class labels---by fitting the product of real-valued prediction functions of pairwise patterns to their similarity. Our formulation of similarity learning does not only generalize many existing ones, but also admits an excess risk bound showing an explicit connection to classification. Finally, we empirically demonstrate the practical usefulness of the proposed method on benchmark datasets.Comment: 22 page