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Inferential Theory of Learning (ITL) is an area of
conclusive, deduction, and induction.
In order for an inference to be considered complete it was required that all categories must be taken into account. This is how the ITL varies from other machine learning theories like Computational Learning Theory and
Inferential Theory of Learning (ITL) is an area of machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
which describes inferential processes performed by learning agents. ITL has been continuously developed by Ryszard S. Michalski
Ryszard S. Michalski (May 7, 1937 – September 20, 2007) was a Polish-American computer scientist. Michalski was Professor at George Mason University and a pioneer in the field of machine learning.
Biography
Michalski was born in Kalusz near Lv ...
, starting in the 1980s. The first known publication of ITL was in 1983. In ITL learning process is viewed as a search (inference
Inferences are steps in reasoning, moving from premises to logical consequences; etymologically, the word '' infer'' means to "carry forward". Inference is theoretically traditionally divided into deduction and induction, a distinction that ...
) through hypotheses space guided by a specific goal. Results of learning need to be stored. Stored information will later be used by the learner for future inference
Inferences are steps in reasoning, moving from premises to logical consequences; etymologically, the word '' infer'' means to "carry forward". Inference is theoretically traditionally divided into deduction and induction, a distinction that ...
s. Inferences are split into multiple categories includinconclusive, deduction, and induction.
In order for an inference to be considered complete it was required that all categories must be taken into account. This is how the ITL varies from other machine learning theories like Computational Learning Theory and
Statistical Learning Theory
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the statistical inference problem of finding a predictive function based on d ...
; which both use singular forms of inference.
Usage
The most relevant published usage of ITL was in scientific journal published in 2012 and used ITL as a way to describe how agent-based learning works. According to the journal "The Inferential Theory of Learning (ITL) provides an elegant way of describing learning processes by agents".References
Further reading
* Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1983), ''Machine Learning: An Artificial Intelligence Approach'', Tioga Publishing Company, . ** Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1986), ''Machine Learning: An Artificial Intelligence Approach, Volume II'', Morgan Kaufmann, . ** Yves Kodratoff, Ryszard S. Michalski (1990), ''Machine Learning: An Artificial Intelligence Approach, Volume III'', Morgan Kaufmann, . ** Ryszard S. Michalski, George Tecuci (1994), ''Machine Learning: A Multistrategy Approach'', Volume IV, Morgan Kaufmann, . ** Naidenova, X. (Ed.),(2009), Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models: Interactive Models, IGI Global,. Cybernetics Learning Machine learning {{Systemstheory-stub