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Statistical classification

classifier

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+#REDIRECT [[Statistical classification]] classifier
-{{Unreferenced|date=November 2009}}
-A '''classifier''' is a function that maps sets of input attributes to tagged classes.
-== Learning and Data Mining ==
-Both [[Data Mining]] and [[Machine learning]] are techniques related to the processing of large amounts of data.
-The [[Data Mining]] technique tries to obtain [[Design pattern (computer science)|patterns]] or models from the data collected.
-[[Machine learning]] is the basic part that the different types of existing classifiers have in common. The basic idea of learning is using the perceptions not only to act but also to improve the ability of an agent to act in the future.
-Learning techniques usually fall into the following categories:
-=== ''Supervised learning'' ===
-The '''[[supervised learning]]''' involves learning a function from tagged examples above, to establish a correspondence between the inputs and these learning system tries to tag (classify) a set of vectors choosing one of several categories ([[Class (computer science)|classes]]).
-=== ''Unsupervised learning'' ===
-The '''[[unsupervised learning]]''' consists in learning from input [[pattern]]s with no output values specified. The main problem of this technique is how to take a decision between all patterns provided. The system takes the input objects as a set of random variables, building a density model for that data set.
-=== ''Semi-supervised learning'' ===
-'''[[Semi-supervised learning]]''' is based on techniques that combine the previous two, this is because in some cases can be very difficult to [[tag]] or classify all the data. The aim is to combine tagged and untagged data to improve modeling. Although it is not always helpful and there are several methods to do so.
-=== ''Reinforcement Learning'' ===
-The '''[[reinforcement learning]]''' is a way of learning by observing the world.
-The idea of learning consists in building a function with the observed behaviour as their input and output. Learning methods can be understood as the research of a rank of hypothesis to find the appropriate function.
-== Type of classifiers ==
-=== ''Bayesian classifier'' ===
-A [[Naive Bayes classifier|Bayesian classifier]] is a [[pattern]] classifier based on statistical theories of learning. Bayesian learning calculates the probability of each hypothesis of the data and makes predictions on this bases. It is an almost optimal learning, but it requires an expensive computational cost because the rank of hypothesis is usually very large, it may even be infinite.
-=== ''Parzen classifier'' ===
-This is a classifier based on nonparametric estimation which obtains the [[probability density function]] of estimating the unknown parameters of a unknown model, this is the basic difference with the parametric estimation, the estimation of the last one is based on a familiar model.
-The technique of this classifier consists of variations in the [[histogram]] approximation of a unknown [[probability density function]]. This algorithm is used in image classification and to classify and learn simultaneously. The problem is that this classifier takes a large runtime.
-=== ''Backpropagation classifier'' ===
-Using simple models or parametric density or [[histogram]]  models do not always give the desired results in some of the observed situations . In these cases, a research of more sophisticated density models needed. [[Neural networks]] are a useful approximation technique to build parametric models of density. The usual [[neural network]] model that uses this algorithm consists of a network with an input layer with as many nodes as inputs have, a hidden layer with a variable number of nodes that depend on the characteristics of the situation, and an output layer with as many nodes as possible outputs have.
-=== ''Classifier with PCA'' ===
-In this classifier PCA method is used ([[Principal Component Analysis]]). This is a technique that aims to reduce the number of variables by transforming the original set of correlated variables (that have some information in common) into a set of uncorrelated variables (without redundancy) called “major components set”. The new variables are linear combinations of the old ones.
-=== ''Support vector machine'' ===
-The [[support vector machine]] (SVM)  is a simple technique that give a  great use when  a classifier is trying to be built with the use of examples. Unlike [[neural networks]] which aim to build a model after an event, the SVM's tries to get the border decision. Its ease is an advantage because you just have to encode the geometry of the border.
-== Applications ==
-The applications of classifiers are wide-ranging. They find use in medicine (drug trial analysis, [[MRI]] [[data analysis]]), finance (share analysis, index [[prediction]]), mobile phones ([[signal decoding]], [[error correction]]), [[computer vision]] ([[face recognition]], [[video tracking|target tracking]]), [[voice recognition]], [[datamining]] (supermarket purchasing analysis, retail customer analysis) and uncountable other areas.
-An example is a classifier that accepts a person's salary details, age, marital status, home address and credit history and classifies the person as acceptable/unacceptable to receive a new credit card or loan.
-For a list of classifier applications and classifier technologies, please see [[statistical classification]].
-==See also==
-* [[Artificial intelligence]]
-* [[Artificial neural network]]
-* [[Data warehouse]]
-* [[Linear classifier]]
-* [[Machine learning]]
-* [[Metadata]]
-* [[Neural network]]
-* [[Pattern recognition]]
-* [[Perceptron]]
-[[Category:Classification algorithms|*]]
-[[Category:Statistical classification]]
-[[ca:Classificador (matemàtiques)]]
-[[de:Klassifikator]]
-[[es:Clasificadores (matemático)]]
-[[eo:Klasigilo (matematiko)]]
-[[fa:شناساگر (ریاضیات)]]
-[[it:Classificatore]]
-[[fi:Luokitin]]
-[[sv:Klassificerare]]
-[[vi:Phân loại (toán học)]]