Semantic and Syntactic Attribute Types in AQ Learning
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| Title: | Semantic and Syntactic Attribute Types in AQ Learning |
| Author(s): | Michalski, Ryszard S.; Wojtusiak, Janusz |
| Keywords: | Attribute types; Machine learning; AQ learning; Natural induction; Computational learning |
| Issue Date: | 18-Nov-2007 |
| Series/Report no.: | Reports of the Machine Learning and Inference Laboratory MLI 07-1 |
| Abstract: | AQ learning strives to perform natural induction that aims at deriving general descriptions from specific data and formulating them in human-oriented forms. Such descriptions are in the forms closely corresponding to simple natural language statements, or are transformed to such statements in order to make computer generated knowledge easy to interpret and understand. An important feature of natural induction is that it employs a wide range of types of attributes to guide the process of generalization. Attribute types constitute problem domain knowledge, and are provided by the user, or are inferred by the learning program from the data. This paper makes a distinction between semantic and syntactic attribute types in AQ learning, explains their relationships and provides their classifications. Semantic types depend solely on the structure of attribute domains and help to create plausible generalizations, while syntactic types depend also on physical properties of attribute domains, and are used to efficiently implement semantic types. |
| URI: | http://hdl.handle.net/1920/2875 |
| Appears in Collections: | Machine Learning and Inference Laboratory |
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