- knowledge classification
- library classification
- archival classification

Types of Classification

The population error (or true error) of a classifier $$f$$, denoted as $$\epsilon(f)$$, is the expected fraction of examples in the entire underlying population for which the classifier makes an incorrect prediction. Given a probability density function $$p(\mathbf{x}, y)$$ for the true data distribution $$P$$, it is calculated as the expectation of the error indicator variable:
$$\epsilon(f) = E_{(\mathbf{x}, y) \sim P} \mathbf{1}(f(\mathbf{x}) 
eq y) = \int\int \mathbf{1}(f(\mathbf{x}) 
eq y) p(\mathbf{x}, y) \;d\mathbf{x} dy$$

Population Error of a Classifier

The sorting of things (concrete and abstract) into groups on the basis of one or more characteristics.

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- Logical reasoning
- Valid and sound arguments
- Formal system of logic
- Classification

Concepts in logic

Satija, M.P.. (2000). Library classification: An essay in terminology. Knowledge Organization. 27. 221-229. 

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