20210103 We can think of algorithms for machine learning along three basic dimensions:
- knowledge representation (the language used to represent models);
- method or search heuristic (how to search different models);
- measure of goodness (how do we know we have a good model).
This basic concept was introduced in (Graham J. Williams 2011) characterising different artificial intelligence and machine learning algorithms in terms of the target language for representing knowledge, how the search space defined by the language is navigated to express sentences in the language, and how the sentences are measured to determine whether we have a good sentence.
In this chapter we present a range of machine learning algorithms, relating them to these dimensions, and demonstrating the algorithms in action.
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0