Multi-norms

Multi-level models for longitudinal growth norms.

Multi-level models for estimating conditional and unconditional longitudinal growth norms are presented. The procedure involves transforming the original growth measurements to Normality and modelling these with a two-level random coefficient model. Growth norms for any desired time interval and function can be derived. Height and weight data are used for illustration.

Overlapping Trace Norms in Multi-View Learning

Multi-view learning leverages correlations between different sources of data to make predictions in one view based on observations in another view. A popular approach is to assume that, both, the correlations between the views and the view-specific covariances have a lowrank structure, leading to inter-battery factor analysis, a model closely related to canonical correlation analysis. We propos...

A Group Norms for Multi-Agent Organisations

Multi-valued t-Norms and t-Conorms

We present a procedure for constructing multi-valued tnorms and t-conorms. Our construction uses a pair of single-valued t-norms and the pair of dual t-conorms to construct interval-valued t-norms u and t-conorms t. In this manner we can combine desirable characteristics of different t-norms and t-conorms; furthermore if we use the t-norm ∧ and t-conorm ∨, then (X,u,t) is a superlattice, i.e. t...

Identifying Conditional Norms in Multi-agent Societies

Most works on norms have investigated how norms are regulated using institutional mechanisms which assume that agents know the norms of the society they are situated in. Few research works have focused on how an agent may infer the norms of a society without the norm being explicitly given to the agent. These works do not address how an agent can identify conditional norms. In this paper we des...