Resultado de búsqueda
15 de may. de 2011 · Towards this goal, we compared accuracy of six different ML algorithms applied to neuroimaging data of persons engaged in a bivariate task, asserting their belief or disbelief of a variety of propositional statements.
15 de may. de 2011 · We apply pattern classifiers towards decoding belief cognitive states from fMRI. Independent Component (IC) Analysis was used for dimension reduction. Six ICs formed a diagnostic, parsimonious feature subset. Informative IC spatial masks were mapped forward. Classification accuracy of belief vs. disbelief was robust and varied by ...
5 de may. de 2011 · We evaluated six machine learning algorithms over a range of complexity, each of which was implemented and automated using Perl, WEKA (University of Waikato, New Zealand), and MATLAB (v.7.6, Mathworks, Inc.) software. We describe each of these briefly here.
1 de nov. de 2010 · Three Machine Learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) and two resample methods: under and over-sampling, were tested.
15 de may. de 2011 · Optimization for K* was varied over percent blending ratio, which corresponds to the sphere of influence in K-NN. We found that a single order polynomial kernel was optimal for SVM, an 86% blending ratio was optimal for K* (Fig. 2), and a 10% pruning ratio for our J48 decision tree.
10 de nov. de 2010 · We performed unsupervised dimension reduction and automated feature extraction using independent component (IC) analysis and extracted IC time courses. Optimization of classification hyperparameters across each classifier occurred prior to assessment.
- "Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief" Fig. 4. Classification accuracy averaged across all subjects, shown for each of the six classifiers as a function of the number of ICs, with fits to 3-parameter first order exponential model (lines).
