Abstract. Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in “nearby” classes in the taxonomy are similar.
In this paper, we present probabilistic abstraction hierarchies (PAH), a probabilisti- cally principled general framework for learning abstraction hierarchies ...
In this paper, we present probabilistic abstraction hierarchies (PAH), a probabilisti- cally principled general framework for learning abstraction hierarchies ...
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Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in "nearby" classes in the taxonomy are similar.
Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in "nearby" classes in the taxonomy are similar.
Jun 6, 2022 · In this paper, we focus on a local case, in which the subroutines have a limited effect on the overall system state.
The abstraction hierarchy includes five levels of information: functional purpose, abstract function, generalized function, physical function and physical form.
Aug 7, 2022 · This paper presents a first verification approach that exploits a specific hierarchical structure natural in many models to accelerate analysing ...
Feb 1, 2023 · We propose Expected Probabilistic Hierarchies (EPH), a probabilistic model to learn hierarchies in data by optimizing expected scores.
Build abstraction hierarchies automatically instead of using manually constructed abstraction hierarchies for text data. Define the neighborhood of an object ...