text: **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. Classifiers[98] 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 "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.[45] 
  
  Expectation–maximization clustering of Old 
  Faithful eruption data starts from a random 
  guess but then successfully converges on 
  an accurate clustering of the two physically 
  distinct modes of eruption. 
  
  There are many kinds of classifiers in use.[99] The decision 
  tree is the simplest and most widely used symbolic machine 
  learning algorithm.[100] 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.[101] The naive Bayes classifier is reportedly 
  the "most widely used learner"[102] at Google, due in part to its scalability.[103] Neural networks are also 
  used as classifiers.[104] 