RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification
نویسندگان
چکیده
منابع مشابه
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification
A Fast and Robust Framework for Hyperspectral Image Classification Faxian Cao1, Zhijing Yang1*, Jinchang Ren2, Wing-Kuen Ling1 1 School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China; [email protected]; [email protected]; [email protected] 2 Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, UK; jinchan...
متن کاملMultinomial Regression Model for In-service Training
In-service training is education for employees to help them develop their professional skills in a specific discipline or occupation. This training takes place after an individual begins work responsibilities. On-line technology is supporting our learning in many ways. Both credit and degree pursuing are formal developing program. There is a need to developing a model of in-service training for...
متن کاملA mixed-effects multinomial logistic regression model.
A mixed-effects multinomial logistic regression model is described for analysis of clustered or longitudinal nominal or ordinal response data. The model is parameterized to allow flexibility in the choice of contrasts used to represent comparisons across the response categories. Estimation is achieved using a maximum marginal likelihood (MML) solution that uses quadrature to numerically integra...
متن کاملRobust Logistic and Probit Methods for Binary and Multinomial Regression.
In this paper we introduce new robust estimators for the logistic and probit regressions for binary, multinomial, nominal and ordinal data and apply these models to estimate the parameters when outliers or inluential observations are present. Maximum likelihood estimates don't behave well when outliers or inluential observations are present. One remedy is to remove inluential observations from ...
متن کاملArchitectural Style Classification Using Multinomial Latent Logistic Regression
Architectural style classification differs from standard classification tasks due to the rich inter-class relationships between different styles, such as re-interpretation, revival, and territoriality. In this paper, we adopt Deformable Part-based Models (DPM) to capture the morphological characteristics of basic architectural components and propose Multinomial Latent Logistic Regression (MLLR)...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational and Mathematical Methods in Medicine
سال: 2021
ISSN: 1748-6718,1748-670X
DOI: 10.1155/2021/5584684