Results 181 to 190 of about 257,684 (210)

Predicting Academic Performance from Future-Oriented Daily Time Management Behavior: A LASSO-Based Study of First-Year College Students

Mingzhang Zuo, Kunyu Wang, Pengxuan Tang, Xiao Meng, Xia Zhou, Heng Luo   +5 more
openalex   +1 more source

Enhanced preoperative prediction for microvascular invasion in hepatocellular carcinoma through an optimized MR Radiomics combination strategy and machine learning predictor. [PDF]

Front Med (Lausanne)
Feng M, Yang Y, Dai Z, Chen Z, Li L, Wu Z, Li X, Guo T, Meng Y, Li Q, Zhao Z, Li T, Zhang J, Kang Y.   +13 more
europepmc   +1 more source

Robustness and Accuracy of Radiomics Models for Classifying IASLC Grading in Lung Adenocarcinomas: A Comprehensive Analysis of a Large Multicenter CT Database. [PDF]

Technol Cancer Res Treat
Fan X, Deng J, Feng Y, Qi W, Lin S, Zeng Y, Zuo Z.   +6 more
europepmc   +1 more source

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Lasso

2021
?? ???????????? ???????????????? ???????????????????? ?????????????????? ?????????????? ???????????????????? LASSO ?? ?????????????????????????????? ?????????????????? ?????????????????????????? ???????????????? ???????? ?? ???????????? ???????????????? ?????????????????????? (??????????????????????????-?????????????????????? ????????????????) CRA. ????
openaire   +3 more sources

Moderately clipped LASSO

Computational Statistics & Data Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kwon, Sunghoon, Lee, Sangin, Kim, Yongdai   +2 more
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Pathway Graphical Lasso

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Graphical models provide a rich framework for summarizing the dependencies among variables. The graphical lasso approach attempts to learn the structure of a Gaussian graphical model (GGM) by maximizing the log likelihood of the data, subject to an l1 penalty on the elements of the inverse covariance matrix.
Maxim, Grechkin, Maryam, Fazel, Daniela, Witten, Su-In, Lee   +3 more
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Group lasso with overlap and graph lasso

Proceedings of the 26th Annual International Conference on Machine Learning, 2009
We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is typically a union of potentially overlapping groups of co-variates defined a priori, or a set of covariates which tend to be connected to each other when a graph of covariates ...
Laurent Jacob, Guillaume Obozinski, Jean-Philippe Vert   +2 more
openaire   +1 more source