A Learning Record Attribute is a Data Attribute of a Learning Record.
- (Wilson, 2008a) ⇒ Bill Wilson. (2008). “The Machine Learning Dictionary for COMP9414." University of New South Wales, Australia.
- attributes: An attribute is a property of an instance that may be used to determine its classification. For example, when classifying objects into different types in a robotic vision task, the size and shape of an instance may be appropriate attributes. Determining useful attributes that can be reasonably calculated may be a difficult job - for example, what attributes of an arbitrary chess end-game position would you use to decide who can win the game? This particular attribute selection problem has been solved, but with considerable effort and difficulty. Attributes are sometimes also called features.
- (Getoor, 2000) ⇒ Lise Getoor. (2000). “Learning Probabilistic Relational Models.” In: 4th International Symposium on Abstraction, Reformulation, and Approximation (SARA 2000). doi:10.1007/3-540-44914-0.
- A PRM, together with a particular database of objects, defines a probability distribution over the attributes of the objects and the relations that hold between them. The relational component describes entities in the model, attributes of each entity, and references from one entity to another. The probabilistic component describes dependencies among attributes, both within the same entity and between attributes in related entities.
- (Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). “Glossary of Terms.” In: Machine Leanring 30(2-3).
- Attribute (field, variable, feature): A quantity describing an instance. An attribute has a domain defined by the attribute type, which denotes the values that can be taken by an attribute. The following domain types are common: