This article is about pattern recognition as a branch of machine statistical fraud detection a review pdf. This article has multiple issues.
Unsourced material may be challenged and removed. KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. In pattern recognition, there may be a higher interest to formalize, explain and visualize the pattern, while machine learning traditionally focuses on maximizing the recognition rates. Yet, all of these domains have evolved substantially from their roots in artificial intelligence, engineering and statistics, and they’ve become increasingly similar by integrating developments and ideas from each other. However, pattern recognition is a more general problem that encompasses other types of output as well. Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform “most likely” matching of the inputs, taking into account their statistical variation. Pattern recognition is generally categorized according to the type of learning procedure used to generate the output value.
Note that sometimes different terms are used to describe the corresponding supervised and unsupervised learning procedures for the same type of output. They output a confidence value associated with their choice. Non-probabilistic confidence values can in general not be given any specific meaning, and only used to compare against other confidence values output by the same algorithm. For a large-scale comparison of feature-selection algorithms see . In order for this to be a well-defined problem, “approximates as closely as possible” needs to be defined rigorously.