Model-based Learning Task
(Redirected from Model Training Task)
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A Model-based Learning Task is a learning task that requires a learned model in a representation language that is more general than the representation language used to describe the training data.
- AKA: Model Induction/Training.
- Context:
- Input: Learning Records.
- optional: a Model Family.
- output: Learned Model.
- It can range from being a Supervised Model-based Learning Task (such as Semi-Supervised Model-based Learning) to being an Unsupervised Model-based Learning Task/Model-based Unsupervised Learning Task.
- It can range from being a Non-Parametric Model Training Task to being a Parametric Model Training Task.
- It can range from being a Parametric Model-based Learning Task/Model-based Parametric Learning Task to being a Non-Parametric Model-based Learning Task/Model-based Non-Parametric Learning Task.
- It can be solved by a Model-based Learning System (that implements a Model-based Learning algorithm).
- …
- Input: Learning Records.
- Example(s):
- Counter-Example(s):
- a Model-Free Learning Task, such as a lazy learning task.
- a Model Deployment Task.
- an Approximate Search Task.
- See: Supervised Learning Task.
References
2003
- (Perlich & Provost, 2003) ⇒ Claudia Perlich, and Foster Provost. (2003). “Aggregation-based Feature Invention and Relational Concept Classes.” In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge discovery and data mining. ISBN:1-58113-737-0 doi:10.1145/956750.956772
- QUOTE: Model induction from relational data requires aggregation of the values of attributes of related entities.