Previous research (e.g. Cognition 64 (1997) 73) suggests that the privileged level for inductive inference in a folk biological conceptual hierarchy does not correspond to the "basic" level (i.e. the level at which concepts are both informative and distinct). To further explore inductive inference within conceptual hierarchies, we examine relations between knowledge of concepts at different hie...

Conversational agents that use reinforcement learning for policy optimization in large domains often face the problem of limited scalability. This problem can be addressed either by using function approximation techniques that estimate an approximate true value function, or by using a hierarchical decomposition of a learning task into subtasks. In this paper, we present a novel approach for dia...

Most current object modeling methods and tools have weaknesses both in the concepts of hierarchical decomposition and in the visualization of these hierarchies. Some methods do not support hierarchical decomposition at all. Those methods which do employ tools that provide explosive zoom as the only means for the visualization of hierarchies. In this paper we present an approach for the visualiz...

The large-scale visual concept detection and annotation task (LS-VCDT) in ImageCLEF 2009 aims at the detection of 53 concepts in consumer photos. These concepts are structured in an ontology which implies a hierarchical ordering and which can be utilized during training and classification of the photos. The dataset consists of 18.000 Flickr photos which were manually annotated with 53 concepts....

We present a hierarchical scheme for synthesis of concept approximations based on given data and domain knowledge. We also propose a solution, founded on rough set theory, to the problem of constructing the approximation of higher level concepts by composing the approximation of lower level concepts. We examine the effectiveness of the layered learning approach by comparing it with the standard...

In this paper a formalism for the speciication of algorithmic concepts recognition is presented. It is coupled to an automated hierarchical concept parsing recognition technique. It allows for a successful handling of the languages and the kind of codes, algorithms and optimizations connected to scientiic and high performance computing. Its exibility and expressivity power for the speciication ...

This paper proposes a method of using ontology hierarchy in automatic topic identification. The fundamental idea behind this work is to exploit an ontology hierarchical structure in order to find a topic of a text. The keywords which are extracted from a given text will be mapped onto their corresponding concepts in the ontology. By optimizing the corresponding concepts, we will pick a single n...

The geometric approach is a mathematical concept developed to improve the analysis and synthesis of linear multivariable systems. In this article we summarized the foundations for the geometric approach to both linear and singular systems. A mathematical introduction to the controlled and conditioned invariant subspaces has been presented. Additional characteristic subspaces were introduced, su...

As the rst step in an automated text sum-marization algorithm, this work presents a new method for automatically identifying the central ideas in a text based on a knowledge-based concept counting paradigm. To represent and generalize concepts, we use the hierarchical concept taxonomy WordNet. By setting appropriate cutoo values for such parameters as concept generality and child-to-parent freq...