Empirically-transformed linear opinion pools

Diversity in Logarithmic Opinion Pools

Logarithmic Opinion Pools for Conditional Random Fields

Since their recent introduction, conditional random fields (CRFs) have been successfully applied to a multitude of structured labelling tasks in many different domains. Examples include natural language processing (NLP), bioinformatics and computer vision. Within NLP itself we have seen many different application areas, like named entity recognition, shallow parsing, information extraction from...

Selecting Weighting Factors in Logarithmic Opinion Pools

A simple linear averaging of the outputs of several networks as e.g. in bagging 3], seems to follow naturally from a bias/variance decomposition of the sum-squared error. The sum-squared error of the average model is a quadratic function of the weighting factors assigned to the networks in the ensemble 7], suggesting a quadratic programmingalgorithm for nding the \optimal"weighting factors. If ...

Normalizing Empirically Underidentified Linear State-Space Models

Normalizing latent-variable models in empirical work has sometimes more influence on statistical inference than commonly appreciated. In this paper, I show how relating non-identification to an invariance property of the likelihood function under certain groups of parameter transformations helps understanding the influence of normalization on inference and can guide the choice of identifying re...

Active learning and logarithmic opinion pools for HPSG parse selection

For complex tasks such as parse selection, the creation of labelled training sets can be extremely costly. Resource-efficient schemes for creating informative labelled material must therefore be considered. We investigate the relationship between two broad strategies for reducing the amount of manual labelling necessary to train accurate parse selection models: ensemble models and active learni...