Classifiers and statistical learning methods Artificial intelligence

the simplest ai applications can divided 2 types: classifiers ( if shiny diamond ) , controllers ( if shiny pick ). controllers do, however, classify conditions before inferring actions, , therefore classification forms central part of many ai systems. classifiers functions use pattern matching determine closest match. can tuned according examples, making them attractive use in ai. these examples known observations or patterns. in supervised learning, each pattern belongs predefined class. class can seen decision has made. observations combined class labels known data set. when new observation received, observation classified based on previous experience.

a classifier can trained in various ways; there many statistical , machine learning approaches. used classifiers neural network, kernel methods such support vector machine, k-nearest neighbor algorithm, gaussian mixture model, naive bayes classifier, , decision tree.

the performance of these classifiers have been compared on wide range of tasks. classifier performance depends on characteristics of data classified. there no single classifier works best on given problems; referred no free lunch theorem. determining suitable classifier given problem still more art science.