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Cross-validation performance for the ( a ) linear discriminant analysis (LDA), ( b ) multilayer layer perceptron (MLP) and ( c ) support vector machine (SVM) classifiers

Journal: BMC Medicine

Article Title: Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder – a systematic methodological exploration of technical and demographic confounders in the search for biomarkers

doi: 10.1186/s12916-018-1086-7

Figure Lengend Snippet: Cross-validation performance for the ( a ) linear discriminant analysis (LDA), ( b ) multilayer layer perceptron (MLP) and ( c ) support vector machine (SVM) classifiers

Article Snippet: Three classification algorithms were implemented using MATLAB, namely LDA, a multilayer perceptron (MLP) neural network with nine nodes in one hidden layer, using the scaled conjugate gradient backpropagation training algorithm, and a support vector machine (SVM) with a nonlinear radial basis function kernel.

Techniques: Biomarker Discovery, Plasmid Preparation

Repeatability analysis for the ( a ) linear discriminant analysis (LDA), ( b ) multilayer layer perceptron (MLP) and ( c ) support vector machine (SVM) classifiers for age-matched sample cross-validation run 1

Journal: BMC Medicine

Article Title: Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder – a systematic methodological exploration of technical and demographic confounders in the search for biomarkers

doi: 10.1186/s12916-018-1086-7

Figure Lengend Snippet: Repeatability analysis for the ( a ) linear discriminant analysis (LDA), ( b ) multilayer layer perceptron (MLP) and ( c ) support vector machine (SVM) classifiers for age-matched sample cross-validation run 1

Article Snippet: Three classification algorithms were implemented using MATLAB, namely LDA, a multilayer perceptron (MLP) neural network with nine nodes in one hidden layer, using the scaled conjugate gradient backpropagation training algorithm, and a support vector machine (SVM) with a nonlinear radial basis function kernel.

Techniques: Plasmid Preparation, Biomarker Discovery

Repeatability analysis for the ( a ) linear discriminant analysis (LDA), ( b ) multilayer layer perceptron (MLP) and ( c ) support vector machine (SVM) classifiers for age-matched sample leave-one-subject-out analysis

Journal: BMC Medicine

Article Title: Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder – a systematic methodological exploration of technical and demographic confounders in the search for biomarkers

doi: 10.1186/s12916-018-1086-7

Figure Lengend Snippet: Repeatability analysis for the ( a ) linear discriminant analysis (LDA), ( b ) multilayer layer perceptron (MLP) and ( c ) support vector machine (SVM) classifiers for age-matched sample leave-one-subject-out analysis

Article Snippet: Three classification algorithms were implemented using MATLAB, namely LDA, a multilayer perceptron (MLP) neural network with nine nodes in one hidden layer, using the scaled conjugate gradient backpropagation training algorithm, and a support vector machine (SVM) with a nonlinear radial basis function kernel.

Techniques: Plasmid Preparation