Multitask Learning is an approach to inductive transfer that improves generalization by using
the domain information contained in the training signals of related tasks as an inductive bias …

With the advent of the Web along with the unprecedented amount of information available in
electronic format, conceptual data analysis is more useful and practical than ever, because …

I present a computational level account of how people combine concepts, and I use this
account to evaluate current models of conceptual combination. Constrained by this account …

Current views of conceptual combination postulate that novel phrases are interpreted by
linking one constituent to another via a relation. For example, robin snakemight be …

The theory of concept (or Galois) lattices provides a simple and formal approach to
conceptual clustering. In this paper we present GALOIS, a system that automates and …

A continuum between purely isolated and purely interrelated concepts is described. Along
this continuum, a concept is interrelated to the extent that it is influenced by other concepts …

Clustering is often used for discovering structure in data. Clustering systems differ in the
objective function used to evaluate clustering quality and the control strategy used to search …

Ease of learning new concepts may best be understood by simultaneously considering
models of learning and theories of how “good” systems of categories are organized. The …

Most natural domains can be represented in multiple ways: we can categorize foods in terms
of their nutritional content or social role, animals in terms of their taxonomic grou**s or …

Posets are discrete mathematical structures which are ubiquitous in a broad range of data
analysis and machine learning applications. Research connecting posets to the data …