FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022696841> ?p ?o ?g. }

• W2022696841 endingPage "e44877" @default.
• W2022696841 startingPage "e44877" @default.
• W2022696841 abstract "Machine learning neuroimaging researchers have often relied on regularization techniques when classifying MRI images. Although these were originally introduced to deal with ill-posed problems it is rare to find studies that evaluate the ill-posedness of MRI image classification problems. In addition, to avoid the effects of the curse of dimensionality very often dimension reduction is applied to the data.Baseline structural MRI data from cognitively normal and Alzheimer's disease (AD) patients from the AD Neuroimaging Initiative database were used in this study. We evaluated here the ill-posedness of this classification problem across different dimensions and sample sizes and its relationship to the performance of regularized logistic regression (RLR), linear support vector machine (SVM) and linear regression classifier (LRC). In addition, these methods were compared with their principal components space counterparts.In voxel space the prediction performance of all methods increased as sample sizes increased. They were not only relatively robust to the increase of dimension, but they often showed improvements in accuracy. We linked this behavior to improvements in conditioning of the linear kernels matrices. In general the RLR and SVM performed similarly. Surprisingly, the LRC was often very competitive when the linear kernel matrices were best conditioned. Finally, when comparing these methods in voxel and principal component spaces, we did not find large differences in prediction performance.We analyzed the problem of classifying AD MRI images from the perspective of linear ill-posed problems. We demonstrate empirically the impact of the linear kernel matrix conditioning on different classifiers' performance. This dependence is characterized across sample sizes and dimensions. In this context we also show that increased dimensionality does not necessarily degrade performance of machine learning methods. In general, this depends on the nature of the problem and the type of machine learning method." @default.
• W2022696841 created "2016-06-24" @default.
• W2022696841 creator A5003498864 @default.
• W2022696841 creator A5041394039 @default.
• W2022696841 date "2012-10-10" @default.
• W2022696841 modified "2023-10-11" @default.
• W2022696841 title "Classification of Structural MRI Images in Alzheimer's Disease from the Perspective of Ill-Posed Problems" @default.
• W2022696841 cites W143603920 @default.
• W2022696841 cites W1554944419 @default.
• W2022696841 cites W1585021961 @default.
• W2022696841 cites W1979062697 @default.
• W2022696841 cites W1987011701 @default.
• W2022696841 cites W1997228011 @default.
• W2022696841 cites W2004421347 @default.
• W2022696841 cites W2007044705 @default.
• W2022696841 cites W2007527993 @default.
• W2022696841 cites W2008152557 @default.
• W2022696841 cites W2009511046 @default.
• W2022696841 cites W2013502943 @default.
• W2022696841 cites W2014661690 @default.
• W2022696841 cites W2015904350 @default.
• W2022696841 cites W2016740629 @default.
• W2022696841 cites W2019583087 @default.
• W2022696841 cites W2027688434 @default.
• W2022696841 cites W2033242948 @default.
• W2022696841 cites W2045185094 @default.
• W2022696841 cites W2056940464 @default.
• W2022696841 cites W2063978378 @default.
• W2022696841 cites W2078524519 @default.
• W2022696841 cites W2079484785 @default.
• W2022696841 cites W2094637188 @default.
• W2022696841 cites W2097360283 @default.
• W2022696841 cites W2107564884 @default.
• W2022696841 cites W2118585731 @default.
• W2022696841 cites W2121148108 @default.
• W2022696841 cites W2122328291 @default.
• W2022696841 cites W2123225824 @default.
• W2022696841 cites W2134946498 @default.
• W2022696841 cites W2140187652 @default.
• W2022696841 cites W2141038809 @default.
• W2022696841 cites W2143826137 @default.
• W2022696841 cites W2150579376 @default.
• W2022696841 cites W2152723280 @default.
• W2022696841 cites W2153171432 @default.
• W2022696841 cites W2153635508 @default.
• W2022696841 cites W2155298532 @default.
• W2022696841 cites W2171831801 @default.
• W2022696841 cites W2341283081 @default.
• W2022696841 cites W2411034702 @default.
• W2022696841 cites W2491057826 @default.
• W2022696841 cites W3015571647 @default.
• W2022696841 cites W3119651796 @default.
• W2022696841 cites W3205656812 @default.
• W2022696841 cites W3214968550 @default.
• W2022696841 cites W740415 @default.
• W2022696841 cites W26205945 @default.
• W2022696841 doi "https://doi.org/10.1371/journal.pone.0044877" @default.
• W2022696841 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3468621" @default.
• W2022696841 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23071501" @default.
• W2022696841 hasPublicationYear "2012" @default.
• W2022696841 type Work @default.
• W2022696841 sameAs 2022696841 @default.
• W2022696841 citedByCount "42" @default.
• W2022696841 countsByYear W20226968412013 @default.
• W2022696841 countsByYear W20226968412014 @default.
• W2022696841 countsByYear W20226968412015 @default.
• W2022696841 countsByYear W20226968412016 @default.
• W2022696841 countsByYear W20226968412017 @default.
• W2022696841 countsByYear W20226968412018 @default.
• W2022696841 countsByYear W20226968412019 @default.
• W2022696841 countsByYear W20226968412020 @default.
• W2022696841 countsByYear W20226968412021 @default.
• W2022696841 countsByYear W20226968412022 @default.
• W2022696841 crossrefType "journal-article" @default.
• W2022696841 hasAuthorship W2022696841A5003498864 @default.
• W2022696841 hasAuthorship W2022696841A5041394039 @default.
• W2022696841 hasBestOaLocation W20226968411 @default.
• W2022696841 hasConcept C105795698 @default.
• W2022696841 hasConcept C111030470 @default.
• W2022696841 hasConcept C114614502 @default.
• W2022696841 hasConcept C119857082 @default.
• W2022696841 hasConcept C12267149 @default.
• W2022696841 hasConcept C129848803 @default.
• W2022696841 hasConcept C139532973 @default.
• W2022696841 hasConcept C153180895 @default.
• W2022696841 hasConcept C154945302 @default.
• W2022696841 hasConcept C22019652 @default.
• W2022696841 hasConcept C27438332 @default.
• W2022696841 hasConcept C33923547 @default.
• W2022696841 hasConcept C41008148 @default.
• W2022696841 hasConcept C48921125 @default.
• W2022696841 hasConcept C50644808 @default.
• W2022696841 hasConcept C54170458 @default.
• W2022696841 hasConcept C70518039 @default.
• W2022696841 hasConcept C74193536 @default.
• W2022696841 hasConcept C83665646 @default.
• W2022696841 hasConcept C95623464 @default.
• W2022696841 hasConceptScore W2022696841C105795698 @default.

• next