Related references
Note: Only part of the references are listed.Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review
Kayleigh K. Hyde et al.
REVIEW JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS (2019)
Cross-validation failure: Small sample sizes lead to large error bars
Gael Varoquaux
NEUROIMAGE (2018)
Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
Gael Varoquaux et al.
NEUROIMAGE (2017)
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Mohammad R. Arbabshirani et al.
NEUROIMAGE (2017)
Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging data, with Autism as an example
Pegah Kassraian-Fard et al.
FRONTIERS IN PSYCHIATRY (2016)
Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy
Etienne Combrisson et al.
JOURNAL OF NEUROSCIENCE METHODS (2015)
Machine learning applications in genetics and genomics
Maxwell W. Libbrecht et al.
NATURE REVIEWS GENETICS (2015)
UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age
Cathie Sudlow et al.
PLOS MEDICINE (2015)
Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises
Daniel Bone et al.
JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS (2015)
Analysis of feature selection stability on high dimension and small sample data
David Dernoncourt et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2014)
A review of microarray datasets and applied feature selection methods
V. Bolon-Canedo et al.
INFORMATION SCIENCES (2014)
Cross-validation pitfalls when selecting and assessing regression and classification models
Damjan Krstajic et al.
JOURNAL OF CHEMINFORMATICS (2014)
Sample size planning for classification models
Claudia Beleites et al.
ANALYTICA CHIMICA ACTA (2013)
Predicting sample size required for classification performance
Rosa L. Figueroa et al.
BMC MEDICAL INFORMATICS AND DECISION MAKING (2012)
Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation
Olivier Devos et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2009)
A review of feature selection techniques in bioinformatics
Yvan Saeys et al.
BIOINFORMATICS (2007)
Bias in error estimation when using cross-validation for model selection
S Varma et al.
BMC BIOINFORMATICS (2006)
Optimal number of features as a function of sample size for various classification rules
JP Hua et al.
BIOINFORMATICS (2005)
Estimating dataset size requirements for classifying DNA microarray data
S Mukherjee et al.
JOURNAL OF COMPUTATIONAL BIOLOGY (2003)
Gene selection for cancer classification using support vector machines
I Guyon et al.
MACHINE LEARNING (2002)
Share Paper
