Knowledge-rich Machine Learning Algorithm: Difference between revisions
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** It can be applied by a [[Knowledge-rich Machine Learning System]] (to solve a [[Knowledge-rich Machine Learning Task]]). | ** It can be applied by a [[Knowledge-rich Machine Learning System]] (to solve a [[Knowledge-rich Machine Learning Task]]). | ||
* <B>See:</B> [[Machine Learning Algorithm]], [[Data-rich Machine Learning Algorithm]], [[Data-Driven Learning Algorithm]], [[Heuristic Learning Algorithm]]. | * <B>See:</B> [[Machine Learning Algorithm]], [[Data-rich Machine Learning Algorithm]], [[Data-Driven Learning Algorithm]], [[Heuristic Learning Algorithm]]. | ||
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Latest revision as of 18:41, 17 September 2021
A Knowledge-rich Machine Learning Algorithm is a machine learning algorithm that can make use of an existing knowledge based.
- Context:
- It can be applied by a Knowledge-rich Machine Learning System (to solve a Knowledge-rich Machine Learning Task).
- See: Machine Learning Algorithm, Data-rich Machine Learning Algorithm, Data-Driven Learning Algorithm, Heuristic Learning Algorithm.
References
2011
- (Griffiths et al., 2011) ⇒ Joshua B. Tenenbaum, Charles Kemp, Thomas L. Griffiths, and Noah D. Goodman. (2011). “How to grow a mind: Statistics, structure, and abstraction." In: Science, 331(6022).
- ABSTRACT: In coming to understand the world — in learning concepts, acquiring language, and grasping causal relations — our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more human-like machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?