What people are saying - Write a review
We haven't found any reviews in the usual places.
Comparison of Machine Learning and Traditional Classifiers in Glaucoma Diagnosis
Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Com
Handling Missing Data with Variational Bayesian Learning of ICA
2 other sections not shown
algorithm approximation automated perimetry SAP backward elimination Bayes CPSD data density data points dataset density estimation density model dimension reduction discriminative classifiers equation feature selection Figure forward selection full dim full missing ICA function of number Gaussian density gaussian SVM glaucoma glaucoma diagnosis hemifield hidden sources hidden variables ICA clusters Independent Component Analysis Jensen's inequality kurtosis Kwokleung Chan learning rules lower bound machine classifiers machine learning maximum likelihood missing data mixed images mixing matrix mixture of Gaussians multilayer perceptron normal optic output overfitting p-values Parzen Window PCA reduced dim PCA reduced dimension performance perimetry polynomial missing ICA probability densities problem Q(ct Q(kt Q(st receiver operating characteristic recovered sources ROC areas ROC curves Sample Sejnowski selection and backward single Gaussian solution source densities standard automated perimetry STATPAC indices Support Vector Machine Te-Won Lee total deviation undercomplete variance variational Bayesian ICA variational Bayesian method variational ICA