In the field of machine learning, an induction algorithm represents an example of using mathematical principles for the development of sophisticated computing systems. Machine learning systems go beyond a simple “rote input/output” function, and evolve the results that they supply with continued use. Induction algorithms can help with the real-time handling of sophisticated data sets, or more long-term efforts.
The induction algorithm is something that applies to systems that show complex results depending on what they are set up for. One of the most fundamental ways that engineers use an induction algorithm is to enhance knowledge acquisition in a given system. In other words, with the algorithm in place, the set of “knowledge data” that end users get is somehow improved, whether that’s regarding the quantity of data, the filtering of noise and undesirable results, or the refinement of some data points.
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Although the technical descriptions of induction algorithms are largely the territory of mathematical and scientific journals, one of the basic ideas about using the induction algorithm is that it can organize “classification rules” according to the induction principle and separate corollary results from different kinds of system noise or exceptions. Filtering out noise from a domain is a prominent use of the induction algorithm in general. There is the idea that in real-world data filtering, induction algorithms can compose different sets of rules for both the legitimate results and the system noise, in order to distinguish one from the other.
By setting up induction algorithms according to certain training examples, stakeholders are looking for the ability of these systems to identify and assess consistent rules and data that represents exceptions to these rules. In a sense, the use of an induction algorithm uses the induction principle to “prove” certain results that can aid knowledge, because they provide more marked delineations in a data set (or multiple data sets) – distinctions that can drive all sorts of end user capabilities.
Like other kinds of machine learning software, induction algorithms are often thought of as a form of “decision support.”
“We consider the principal task of a real-world induction system to be assisting the expert in expressing his or her expertise,” write the authors of a Turing Institute paper on induction in machine learning back in the 1980s. “Consequently, we require that the induced rules are highly predictive and are easily comprehensible to the expert.”
With this in mind, induction algorithms can be part of many kinds of software products that seek to refine data and produce evolving results for human users. In general, machine learning and the use of visual dashboards is generating new tools through which users can more rapidly develop in-depth knowledge about any given system, whether it's related to marine research, medical diagnosis, e-commerce, or any other kind of data-rich system.