Attribute-Value Learning Record

AKA: Feature Tuple.
Context:
- It can (typically) represent a Feature Space.
- It can be a member of a Flat Learning Record Set.
- It can range from being a Binary Feature Vector to being a Nominal Feature Vector to being an Integer Feature Vector to being a Real-Value Feature Vector.
- It can range from (typically) being a Fixed-Length Feature Tuple to being a Dynamic-Length Feature Tuple.
- …
Example(s):
- a Categorical Learning Record, such as: <'Red','John Smith','False'>
- a Vectorized Learning Record, such as: <2,29,0.1>.
- a Hybrid Learning Record, such as: <'Red',2,'John Smith',29,0.1,'False'>
Counter-Example(s):
- a Text Item.
See: Training Record, Testing Record.

References

(Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). “Glossary of Terms.” In: Machine Leanring 30(2-3).
- QUOTE: Feature vector (record, tuple): A list of features describing an instance.

(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.