Inductive reasoning

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Inductive reasoning

The attempt to use information about a specific situation to draw a conclusion.

Inductive Reasoning

A way of forming reasonable conclusions by gathering evidence and then forming principles based upon them. For example, if one wishes to find out how a stock will perform, one gathers as much evidence on that stock as possible and makes a conclusion based on that, regardless of one's feelings or suppositions beforehand. The advantage of inductive reasoning is that its evidence offers applicability to "real world" scenarios; however, a disadvantage is that one's evidence may be inaccurate or anecdotal. It is sometimes difficult to know how much evidence is needed to justify coming to a general conclusion. See also: Deductive reasoning, Analogy.
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That is, junior secondary students could be, should be, introduced to problems and thinking related to inductive logic.
Structure-activity relationships derived by machine learning: The use of atoms and their bond connectives to predict mutagenicity by inductive logic programming.
The universality of the law of causation is "the source from which the canons of the Inductive Logic derive their validity" (Mill, 1974, p.
Thus, in philosophy, the control aspect of inductive logic has been under active investigation, whilst the inference component has been set aside.
Hansen's theory of employment is based on an empirical epistemology and inductive logic.
This exercise draws on economic and political theory and inductive logic based on the Wisconsin case to produce a useful contribution to the literatures on migration effects and the determinants of redistributive policy.
While there are many examples were a science split from philosophy and became autonomous (such as physics with Newton and biology with Darwin), and while there are, perhaps, topics that are of exclusively philosophical interest, inductive logic - as this handbook attests - is a research field where philosophers and scientists fruitfully and constructively interact.
Among the topics are an incomplete and simplifying introduction to linked data, learning onto-relational rules with inductive logic programming, information extraction for ontology learning, the semantic enrichment of places from public places descriptions to linked data, and formal concept analysis methods for interactive ontology learning.
The following sections presents the evolution of tools and techniques from inductive logic programming and relational data mining through special purpose systems for bioinformatics to general purpose semantic data mining approaches which enable the use of domain ontologies as background knowledge for data analysis.
He introduces the symbolic logic most courses and texts emphasize, but also other areas of logic, such as sentential and predicate logic, probability and inductive logic, inference to the best explanation, and topics in informal logic such as fallacy detection and learning how to penetrate the fog of political rhetoric and spin.
Taking up themes from his work on inductive logic, Rescher argues in Chapter 6 for the usefulness of presumption, since deductive reasoning cannot get us very far in our cognitive project.
An inductive logic programming approach to statistical relational learning.