Add to ORKGLearning hierarchical concepts is a central problem in cognitive science. This paper explores the Nearest-Merge algorithm for creating hierarchical clusters that can handle both feature-based and relational information, building on the SAGE model of analogical generalization. We describe its results on three data sets, showing that it provides reasonable fits with human data and comparable results to Bayesian models
This paper develops a new method for hierarchical clustering. Unlike other existing clustering schem...
This paper develops a new method for hierarchical clustering. Unlike other existing clustering schem...
Fast and efficient learning over large bodies of commonsense knowledge is a key requirement for cogn...
Concept learning is a central problem for cognitive systems. Generalization techniques can help orga...
Item does not contain fulltextOne prominent account of concept and category learning is that concept...
Most associative memory models perform one level mapping between predefined sets of input and output...
We demonstrate the potential of using hierarchical Bayesian methods to relate models and data in the...
My primary research motivation is the development of a truly generic Machine Learning engine. Toward...
Brains and computers represent and process sensory information in different ways. Bridgingthat gap i...
An algorithm is described which constructs a hierarchical taxonomy over object sets. The algorithm f...
This article demonstrates the potential of using hierarchical Bayesian methods to relate models and ...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
The structure of people's conceptual knowledge of concrete nouns has traditionally been viewed ...
In this thesis, a new system for incremental conceptual clustering is presented. Incremental concept...
Concept learning and organization are much studied in artificial intelligence and cognitive psycholo...
This paper develops a new method for hierarchical clustering. Unlike other existing clustering schem...
This paper develops a new method for hierarchical clustering. Unlike other existing clustering schem...
Fast and efficient learning over large bodies of commonsense knowledge is a key requirement for cogn...
Concept learning is a central problem for cognitive systems. Generalization techniques can help orga...
Item does not contain fulltextOne prominent account of concept and category learning is that concept...
Most associative memory models perform one level mapping between predefined sets of input and output...
We demonstrate the potential of using hierarchical Bayesian methods to relate models and data in the...
My primary research motivation is the development of a truly generic Machine Learning engine. Toward...
Brains and computers represent and process sensory information in different ways. Bridgingthat gap i...
An algorithm is described which constructs a hierarchical taxonomy over object sets. The algorithm f...
This article demonstrates the potential of using hierarchical Bayesian methods to relate models and ...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
The structure of people's conceptual knowledge of concrete nouns has traditionally been viewed ...
In this thesis, a new system for incremental conceptual clustering is presented. Incremental concept...
Concept learning and organization are much studied in artificial intelligence and cognitive psycholo...
This paper develops a new method for hierarchical clustering. Unlike other existing clustering schem...
This paper develops a new method for hierarchical clustering. Unlike other existing clustering schem...
Fast and efficient learning over large bodies of commonsense knowledge is a key requirement for cogn...