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Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints.
Researchers and analysts typically use RQDA using two types of coding approaches: inductive and deductive. In inductive coding, a researcher codes a body of text "from the ground up". That is, the textual units that are coded are not pre-determined by specific theory/literature/concepts but the texts are coded to discover new concepts/ideas ...
Inductive logic programming has adopted several different learning settings, the most common of which are learning from entailment and learning from interpretations. [16] In both cases, the input is provided in the form of background knowledge B, a logical theory (commonly in the form of clauses used in logic programming), as well as positive and negative examples, denoted + and respectively.
Like most research methods, the process of thematic analysis of data can occur both inductively or deductively. [1] In an inductive approach, the themes identified are strongly linked to the data. [4] This means that the process of coding occurs without trying to fit the data into pre-existing theory or framework.
Inductive logic programming (ILP) is an approach to machine learning that induces logic programs as hypothetical generalisations of positive and negative examples. Given a logic program representing background knowledge and positive examples together with constraints representing negative examples, an ILP system induces a logic program that ...
Grounded theory combines traditions in positivist philosophy, general sociology, and, particularly, the symbolic interactionist branch of sociology.According to Ralph, Birks and Chapman, [9] grounded theory is "methodologically dynamic" [7] in the sense that, rather than being a complete methodology, grounded theory provides a means of constructing methods to better understand situations ...
Axial coding in grounded theory is the process of relating codes (categories and concepts) to each other, via a combination of inductive and deductive thinking. According to Strauss and Corbin (1990, 1998) who propose the use of a "coding paradigm", the basic framework of generic relationships is understood to include categories related to:
Decision Tree. Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data.