Search results for “трансдуктивный SVM”

The clustering problem and its ill-posedness, the aims of clustering, types of cluster structure, sensitivity to the choice of metric; the problem of semi-supervised learning and transductive learning, why it reduces neither to classification nor to clustering; criteria of clustering quality, the silhouette coefficient, the BCubed measures of precision and recall; the k-means method (Lloyd's algorithm) as a simplification of the EM algorithm for a mixture of Gaussians; the DBSCAN algorithm; agglomerative hierarchical clustering, the Lance–Williams formula and its special cases, the dendrogram, monotonicity, contracting and dilating distances, Ward's distance; semi-supervised learning based on classification: self-training, co-training, co-learning, the transductive SVM, expectation regularisation (XR).

A survey lecture: machine learning as formulations of optimisation problems, a model, a loss function, a regulariser. Regression and classification, continuous approximations of the threshold loss function, SVM and logistic regression, quantile, robust and SVM regression; the L0, L1, L2, Elastic Net, SFM, RFM regularisers and feature selection; learning to rank: ordinal regression, the pairwise approach and AUC, LambdaRank; estimating a density and a mixture of distributions, clustering as a special case of a mixture, the one-class SVM; semi-supervised learning, the transductive SVM, PU-learning; matrix factorisations, autoencoders, multidimensional scaling, graph factorisations, GraphEDM; transfer learning, multi-task and self-supervised learning, distillation, learning using privileged information, generative adversarial networks; a typology of machine learning problems.