| Statistical Classification |
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| CATEGORIES ABOUT STATISTICAL CLASSIFICATION | |
| machine learning | |
| classification algorithms | |
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Formally, the problem can be stated as follows: given training data produce a classifier which maps an object to its classification label . For example, if the problem is filtering spam, then is some representation of an email and is either "Spam" or "Non-Spam". Statistical classification algorithms are typically used in Pattern Recognition systems. Note: in Community Ecology , the term "classification" is synonymous with what is commonly known (in Machine Learning ) as Clustering . See that article for more information about purely Unsupervised techniques. STATISTICAL CLASSIFICATION TECHNIQUES While there are many methods for classification, they are solving one of three related mathematical problems The first is to find a map of a feature space (which is typically a multi-dimensional Vector Space ) to a set of labels. This is equivalent to partitioning the feature space into regions, then assigning a label to each region. Such algorithms (e.g., the Nearest Neighbour Algorithm ) typically do not yield confidence or Class Probabilities , unless post-processing is applied. Another set of algorithms to solve this problem first apply Unsupervised Clustering to the feature space, then attempt to label each of the clusters or regions. The second problem is to consider classification as an Estimation problem, where the goal is to estimate a function of the form | ||
| :<math>P({ M Class}{ Ec X}) | \int f\left( ec x ec heta
ight)P( ec hetaD) d ec heta</math> |
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| The Third Problem Is Related To The Second, But The Problem Is To Estimate The | "http://wwwinformationdelightinfo/information/entry/conditional_probability" class="copylinks">Class-conditional Probabilities <math>P( ec x{
m class})</math> and then use Bayes' Rule to produce the class probability as in the second problem |
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