Editing Artificial intelligence (section)

=== Classifiers and statistical learning methods ===
The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. [[Classifier (mathematics)|Classifiers]]<ref>Statistical learning methods and [[Classifier (mathematics)|classifiers]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 20}},</ref> are functions that use [[pattern matching]] to determine the closest match. They can be fine-tuned based on chosen examples using [[supervised learning]]. Each pattern (also called an "[[random variate|observation]]") is labeled with a certain predefined class. All the observations combined with their class labels are known as a [[data set]]. When a new observation is received, that observation is classified based on previous experience.<ref name="Supervised learning"/>

There are many kinds of classifiers in use.<ref>{{Cite book |last1=Ciaramella |first1=Alberto |author-link=Alberto Ciaramella |title=Introduction to Artificial Intelligence: from data analysis to generative AI |last2=Ciaramella |first2=Marco |date=2024 |publisher=Intellisemantic Editions |isbn=978-8-8947-8760-3}}</ref> The [[decision tree]] is the simplest and most widely used symbolic machine learning algorithm.<ref>[[Alternating decision tree|Decision tree]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 19.3}}, {{Harvtxt|Domingos|2015|p=88}}</ref> [[K-nearest neighbor]] algorithm was the most widely used analogical AI until the mid-1990s, and [[Kernel methods]] such as the [[support vector machine]] (SVM) displaced k-nearest neighbor in the 1990s.<ref>[[Nonparametric statistics|Non-parameteric]] learning models such as [[K-nearest neighbor]] and [[support vector machines]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 19.7}}, {{Harvtxt|Domingos|2015|p=187}} (k-nearest neighbor)
* {{Harvtxt|Domingos|2015|p=88}} (kernel methods)</ref>
The [[naive Bayes classifier]] is reportedly the "most widely used learner"{{Sfnp|Domingos|2015|p=152}} at Google, due in part to its scalability.<ref>[[Naive Bayes classifier]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 12.6}}, {{Harvtxt|Domingos|2015|p=152}}</ref>
[[Artificial neural network|Neural networks]] are also used as classifiers.<ref name="Neural networks"/>