Recognition Function
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A Recognition Function is a function structure that is a detection function and a classification function structure.
- AKA: Recognizer Model.
- Context:
- It can be created by a Recognition Model Creation Task.
- It can (often) be used to detect the presence of a Pattern in an Artifact.
- Example(s):
- Counter-Example(s):
- See: Detector Function, Classification Function.
References
2005
- (Strasburder, 2005) ⇒ Hans Strasburger. (2005). “Unfocussed Spatial Attention Underlies the Crowding Effect in Indirect Form Vision.” In: Journal of Vision, 5(11):8.
- In a hierarchy of task complexity ranging from
- (1) pattern detection (present/nonpresent),
- (2) coarse grating discrimination1 (horizontal/vertical),
- (3) fine grating discrimination (orientation threshold), and
- (4) character recognition or identification,
- In a hierarchy of task complexity ranging from
2002
- (Roth & Yih, 2002) ⇒ Dan Roth, and Wen-tau Yih. (2002). “Probabilistic Reasoning for Entity & Relation Recognition.” In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2002).
- QUOTE: In all earlier works we know of, the tasks of identifying entities and relations were treated as separate problems. The common procedure is to first identify and classify entities using a named entity recognizer and only then determine the relations between the entities. However, this approach has several problems. First, errors made by the named entity recognizer propagate to the relation classifier and may degrade its performance significantly. For example, if “Boston” is mislabeled as a person, it will never be classified as the location of Poe’s birthplace. Second, relation information is sometimes crucial to resolving ambiguous named entity recognition. For instance, if the entity “JFK” is identified as the victim of the assassination, the named entity recognizer is unlikely to misclassify it as a location (e.g. JFK airport).
2000
- (Valpola, 2000) ⇒ Harri Valpola. (2000). “Bayesian Ensemble Learning for Nonlinear Factor Analysis." PhD Dissertation, Helsinki University of Technology.
- QUOTE: recognition model: A model which states how features can be obtained from the observations. See generative model.
- (Valpola, 2000) ⇒ Harri Valpola. (2000). “Bayesian Ensemble Learning for Nonlinear Factor Analysis." PhD Dissertation, Helsinki University of Technology.
- QUOTE: recognition model: A model which states how features can be obtained from the observations. See generative model.
1970
- (Earley, 1970) ⇒ Jay Earley. (1970). “An Efficient Context-Free Parsing Algorithm.” In: Communications of the ACM, 13(2). doi:10.1145/362007.362035
- QUOTE: A recognizer is an algorithm which takes as input a string and either accepts or rejects it depending on whether or not the string is a sentence of the grammar.