SUSTAIN: A Network Model of Category Learning.
The University of Texas at Austin
Abstract
SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully…
Citation impact
- FWCI
- 34.22
- Percentile
- 100%
- References
- 134
Authors
3Topics & keywords
- Unsupervised learning
- Artificial intelligence
- Inference
- Event (particle physics)
- Concept learning
- Identification (biology)
- Machine learning
- Social learning