Journal ArticleDOI
Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images.
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TLDR
It is shown that the most accurate characterizations are achieved by using prior knowledge of where to expect neurodegeneration (hippocampus and parahippocampal gyrus) and that feature selection does improve the classification accuracies, but it depends on the method adopted.About:
This article is published in NeuroImage.The article was published on 2012-03-01. It has received 288 citations till now. The article focuses on the topics: Feature (computer vision) & Feature selection.read more
Citations
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Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review
TL;DR: Support-Vector-Machine has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data, and those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease and autistic spectrum disorder are reviewed.
Journal ArticleDOI
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.
Journal ArticleDOI
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
TL;DR: The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression, and a detailed account of AD classification challenges is provided.
Journal ArticleDOI
A Review of Feature Reduction Techniques in Neuroimaging
TL;DR: Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability and in this review, feature reduction techniques used with machine learning in neuroimaging studies are discussed.
Journal ArticleDOI
PRoNTo: Pattern Recognition for Neuroimaging Toolbox
Jessica Schrouff,Maria J. Rosa,Jane M. Rondina,Jane M. Rondina,Andre F. Marquand,Carlton Chu,John Ashburner,Christophe Phillips,Jonas Richiardi,Jonas Richiardi,Janaina Mourao-Miranda,Janaina Mourao-Miranda +11 more
TL;DR: The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models, and to facilitate novel contributions from developers, aiming to improve the interaction between the neuroim imaging and machine learning communities.
References
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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Regression Shrinkage and Selection via the Lasso
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The Nature of Statistical Learning Theory
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
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