In this post I will cover some basic machine learning definitions to help me understand things moving forward.
Here, the goal of the algorithm is to use information about a given item or data instance, and assign it a result or category (Laird, van Lent, 2005).
Similar to classification, but the resulting categories are not pre-defined, and multiple data instances are used to define groups (Laird, van Lent, 2005).
Modifying function input variables to find the highest or most optimal output result.
A decision tree represents a process of classification through a mufti-step decision-making tree (SAS Institute).
With respect to machine learning, a rule is something that an system may define based on observations that defines how it should react to a given input.
Generally requires specific hardware. Neural networks learn by example rather than programming, by simulating a highly interconnected network similar to the human brain (Siganos , D and Stergiou, C).
A model is a data set generated by the computer which is used to represent something. For example, a player model could be a set of variables that are set in order to represent the behaviours of a player.
Laird, John; van Lent, Michael. “Machine Learning for Computer Games.” Game Developers Conference. Moscone Center West, San Francisco, CA. 10 Mar. 2005. Lecture.
“Decision Trees— What Are They?.” . Statistical Analysis System Institute. Web. <http://support.sas.com/publishing/pubcat/chaps/57587.pdf>
Siganos , Dimitrios , and Stergiou, Christos . “Neural Networks.” Neural Networks. N.p., n.d. Web. 18 June 2014. <http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html>.