In top-down inductive learning methods how many literals are available? What are they?

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There are three literals available in top-down inductive learning methods they are

a) Predicates

b) Equality and Inequality

c) Arithmetic Literals

In top-down inductive learning methods, the number of literals available depends on the specific approach being used and the characteristics of the dataset being analyzed. However, typically, there are two main types of literals:

  1. Positive Literals: These literals represent positive instances or features present in the data. For example, if you’re analyzing a dataset of animals and one of the features is “has fur,” a positive literal might be “has fur = true.”
  2. Negative Literals: These literals represent negative instances or features absent in the data. Following the same example, a negative literal might be “has fur = false.”

These literals form the basis for hypotheses or rules generated during the learning process. They are used to build a logical representation of the patterns present in the data, which can then be used for classification or prediction tasks. The specifics of the literals will vary depending on the domain and the problem being tackled.