Results 21 to 30 of about 2,017,337 (259)
Hidden Markov Model Based on Logistic Regression
Mathematics, 2023 A hidden Markov model (HMM) is a useful tool for modeling dependent heterogeneous phenomena. It can be used to find factors that affect real-world events, even when those factors cannot be directly observed.
Byeongheon Lee, Joowon Park, Yongku Kim
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Uncertain logistic regression models
AIMS MathematicsLogistic regression is a generalized nonlinear regression analysis model and is often used for data mining, automatic disease diagnosis, economic prediction, and other fields.
Jinling Gao , Zengtai Gong
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Leukemia prediction using sparse logistic regression.
PLoS ONE, 2013 We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in ...
Tapio Manninen +3 more
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Supporting Regularized Logistic Regression Privately and Efficiently. [PDF]
PLoS ONE, 2016 As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on.
Wenfa Li +3 more
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Enabling Equal Opportunity in Logistic Regression Algorithm
Management, 2023 Research Question: This paper aims at adjusting the logistic regression algorithm to mitigate unwanted discrimination shown towards race, gender, etc. Motivation: Decades of research in the field of algorithm design have been dedicated to making a better
Sandro Radovanović, Marko Ivić
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The sign of the logistic regression coefficient
, 2014 Let Y be a binary random variable and X a scalar. Let $\hat\beta$ be the maximum likelihood estimate of the slope in a logistic regression of Y on X with intercept.
Owen, Art B., Roediger, Paul A.
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On Data-Enriched Logistic Regression
MathematicsBiomedical researchers typically investigate the effects of specific exposures on disease risks within a well-defined population. The gold standard for such studies is to design a trial with an appropriately sampled cohort.
Cheng Zheng +4 more
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Distributed Parallel Sparse Multinomial Logistic Regression
IEEE Access, 2019 Sparse Multinomial Logistic Regression (SMLR) is widely used in the field of image classification, multi-class object recognition, and so on, because it has the function of embedding feature selection during classification.
Dajiang Lei +4 more
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Network topology drives population temporal variability in experimental habitat networks
Population Ecology, EarlyView.Habitat patches connected by dispersal pathways form habitat networks. We explored how network topology affects population outcomes in laboratory experiments using a model species (Daphnia carinata). Central habitat nodes in complex lattice networks exhibited lower temporal variability in population sizes, suggesting they support more stable ...
Yiwen Xu +3 more
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Sparse Bilinear Logistic Regression [PDF]
, 2014 In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems involving explanatory variables that are two-dimensional matrices.
Baraniuk, Richard G. +2 more
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