Results 281 to 290 of about 118,919 (305)
Some of the next articles are maybe not open access.
Multinomial Latent Logistic Regression for Image Understanding
IEEE Transactions on Image Processing, 2016In this paper, we present multinomial latent logistic regression (MLLR), a new learning paradigm that introduces latent variables to logistic regression. By inheriting the advantages of logistic regression, MLLR is efficiently optimized using the second-order derivatives and provides effective probabilistic analysis on output predictions.
Zhe, Xu +5 more
openaire +2 more sources
Comparative analysis using multinomial logistic regression
2014 2nd International Conference on Business and Information Management (ICBIM), 2014Financial sustainability can be ensured through adopting financial control measures. Therefore it is necessary to examine whether an ULB has exercised financial controls during a particular year. In our paper we have examined whether an ULB has been able to exercise our designed financial controls. We have categorised the ULBs in “Financial Control and
Sidhakam Bhattacharyya +1 more
openaire +1 more source
Multinomial Logistic Regression (55 Patients)
2012Primary question: the numbers of patients falling out of bed with and without injury are assessed in two hospital departments. It is expected that the department of internal medicine will have higher scores.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire +1 more source
Sentiment analysis using multinomial logistic regression
2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), 2017Data amount becomes rapidly increased in today's era. Data can be in form of text, picture, voice, and video. Social media is one factor of the data increase as everybody expresses, gives opinion, and even complains in social media. The first step is data collection used API twitter with each candidate names on Jakarta Governor Election.
W.P. Ramadhan +2 more
openaire +1 more source
A mixed‐effects multinomial logistic regression model
Statistics in Medicine, 2003AbstractA 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.
openaire +2 more sources
Classification Efficiency of Multinomial Logistic Regression Relative to Ordinal Logistic Regression
Journal of the American Statistical Association, 1989Abstract Classification procedures are useful for the prediction of a response (or outcome) as a result of knowledge of the levels of one or more independent (or predictor) variables. The procedure is said to classify the (possibly multivariate) observation to a level of the response variable. An example might be the prediction of whether an individual
M. Karen Campbell, Allan Donner
openaire +1 more source
A Goodness‐of‐Fit Test for Multinomial Logistic Regression
Biometrics, 2006Summary This article presents a score test to check the fit of a logistic regression model with two or more outcome categories. The null hypothesis that the model fits well is tested against the alternative that residuals of samples close to each other in covariate space tend to deviate from the model in the same direction.
Goeman, Jelle J., Le Cessie, Saskia
openaire +2 more sources
Special restrictions in multinomial logistic regression [PDF]
Stata Technical Bulletin, 2000 Item does not contain ...
openaire +1 more source