Titlebook: Classification - the Ubiquitous Challenge; Proceedings of the 2 Claus Weihs,Wolfgang Gaul Conference proceedings 2005 Springer-Verlag Berli

micronized

https://doi.org/10.1007/978-3-319-31954-4pplications. Aggregating these to a “common” solution amounts to finding a consensus clustering, which can be characterized in a general optimization framework. We discuss recent conceptual and computational advances in this area, and indicate how these can be used for analyzing the structure in cluster ensembles by clustering its elements.

初次登臺(tái)

airborne

crucial

搬運(yùn)工

Bayesian Mixed Membership Models for Soft Clustering and Classification assumptions on four levels: population, subject, latent variable, and sampling scheme. Population level assumptions describe the general structure of the population that is common to all subjects. Subject level assumptions specify the distribution of observable responses given individual membership

Amorous

Predicting Protein Secondary Structure with Markov Modelslices or coils. Spacial and other properties are described by the higher order structures. The classification task we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown pr