Results 21 to 30 of about 2,178,475 (325)
Kernel Logistic Regression-linear for Leukemia Classification Using High Dimensional Data [PDF]
, 2009 Kernel Logistic Regression (KLR) is one of the statistical models that has been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernel–machine techniques.
Embong, A. (A) +3 more
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Logistic regression diagnostics in ridge regression
Computational Statistics, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. Revan Özkale +2 more
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Oblivious sketching for logistic regression
CoRR, 2021 What guarantees are possible for solving logistic regression in one pass over a data stream? To answer this question, we present the first data oblivious sketch for logistic regression. Our sketch can be computed in input sparsity time over a turnstile data stream and reduces the size of a $d$-dimensional data set from $n$ to only $\operatorname{poly ...
Alexander Munteanu +2 more
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BarekengThe Public Health Development Index (Indeks Pembangunan Kesehatan Masyarakat - IPKM) is a combined parameter that reflects progress in health development and is useful for determining areas that need assistance in improving health development.
Erwan Setiawan +2 more
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The Origins of Logistic Regression [PDF]
SSRN Electronic Journal, 2003 This paper describes the origins of the logistic function, its adoption in bio-assay, and its wider acceptance in statistics. Its roots spread far back to the early 19th century; the survival of the term logistic and the wide application of the device have been determined decisively by the personal histories and individual actions of a few scholars.
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Conditional logistic regression [PDF]
American Journal of Orthodontics and Dentofacial Orthopedics, 2017 In this article, we will describe how to analyze binary data from matched studies in orthodontics. We have previously discussed matched analysis for paired binary data (McNemar test), but now we will focus on the use of regression methods to model our data.1 The idea is the same as with simple logistic regression models for binary data2,3; however, we ...
Koletsi D, Pandis N
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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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On the Complexity of Logistic Regression Models [PDF]
Neural Computation, 2019 We investigate the complexity of logistic regression models, which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997 ). We find that the complexity of logistic models with binary inputs depends not only on the number of parameters but also on the distribution of inputs in a ...
Nicola Bulso +2 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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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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