Concept Learning Task
A Concept Learning Task is a learning task that results in concept knowledge.
- Context:
- It can range from being a Simple Concept Learning Task to being a Complex Concept Learning Task.
- It can range from being a Heuristic Concept Learning Task to being a Data-Driven Concept Learning Task.
- …
- Example(s):
- Counter-Example(s):
- See: Inductive Learning; Recognition Task; Exemplar Theory.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/concept_learning Retrieved:2015-7-2.
- Concept learning, also known as category learning, concept attainment, and concept formation, is largely based on the works of the cognitive psychologist Jerome Bruner. Bruner, Goodnow, & Austin (1967) defined concept attainment (or concept learning) as "the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories." More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on the understanding that each object, event, or idea has a set of common relevant features. Thus, concept learning is a strategy which requires a learner to compare and contrast groups or categories that contain concept-relevant features with groups or categories that do not contain concept-relevant features.
Concept learning also refers to a learning task in which a human or machine learner is trained to classify objects by being shown a set of example objects along with their class labels. The learner simplifies what has been observed by condensing it in the form of an example. This simplified version of what has been learned is then applied to future examples. Concept learning may be simple or complex because learning takes place over many areas. When a concept is difficult, it is less likely that the learner will be able to simplify, and therefore will be less likely to learn. Colloquially, the task is known as learning from examples. Most theories of concept learning are based on the storage of exemplars and avoid summarization or overt abstraction of any kind.
- Concept learning, also known as category learning, concept attainment, and concept formation, is largely based on the works of the cognitive psychologist Jerome Bruner. Bruner, Goodnow, & Austin (1967) defined concept attainment (or concept learning) as "the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories." More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on the understanding that each object, event, or idea has a set of common relevant features. Thus, concept learning is a strategy which requires a learner to compare and contrast groups or categories that contain concept-relevant features with groups or categories that do not contain concept-relevant features.
2007
- http://edutechwiki.unige.ch/en/Concept_learning#Definitions
- QUOTE: Concept learning is one major learning type. While teaching simple concepts with clear instances is not that difficult, teaching concepts border cases is difficult, and teaching complex concepts remains a major challenge.
Concept learning encompasses learning how to discriminate and categorize things (with critical attributes). It also involves recall of instances, integration of new examples and sub-categorization. Concept formation is not related to simple recall, it must be constructed.
- QUOTE: Concept learning is one major learning type. While teaching simple concepts with clear instances is not that difficult, teaching concepts border cases is difficult, and teaching complex concepts remains a major challenge.
2011
- (Sammut, 2011b) ⇒ Claude Sammut. (2011). “Concept Learning.” In: (Sammut & Webb, 2011) p.205
- QUOTE: The term concept learning is originated in psychology, where it refers to the human ability to learn categories for object and to recognize new instances of those categories. In machine learning, concept is more formally defined as “inferring a boolean-valued function from training examples of its inputs and outputs” (Mitchell, 1997).
1999
- (Domingos, 1996) ⇒ Pedro Domingos. (1996). “Unifying Instance-based and Rule-based Induction.” In: Machine Learning, 24(2). doi:10.1023/A:1018006431188
- QUOTE: Inductive learning is the explicit or implicit creation of general concept or class descriptions from examples. Many induction problems can be described as follows. A training set of preclassified examples is given, where each example (also called observation or case) is described by a vector of features or attribute values, and the goal is to form a description that can be used to classify previously unseen examples with high accuracy.
1956
- (Seymour & Austin, 1956) ⇒ Jerome Bruner Seymour, and George Allen Austin. (1956). “A Study of Thinking." Transaction Publishers