Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Making large-scale support vector machine learning practical
Advances in kernel methods
Training products of experts by minimizing contrastive divergence
Neural Computation
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Learning to Decode Cognitive States from Brain Images
Machine Learning
A fast learning algorithm for deep belief nets
Neural Computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
Classification using discriminative restricted Boltzmann machines
Proceedings of the 25th international conference on Machine learning
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We compare 10 methods of classifying fMRI volumes by applying them to data from a longitudinal study of stroke recovery: adaptive Fisher's linear and quadratic discriminant; gaussian naive Bayes; support vector machines with linear, quadratic, and radial basis function (RBF) kernels; logistic regression; two novel methods based on pairs of restricted Boltzmann machines (RBM); and K-nearest neighbors. All methods were tested on three binary classification tasks, and their out-of-sample classification accuracies are compared. The relative performance of the methods varies considerably across subjects and classification tasks. The best overall performers were adaptive quadratic discriminant, support vector machines with RBF kernels, and generatively trained pairs of RBMs.