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The nearest neighbor algorithm in pattern recognition is a method for classifying phenomena based upon observable features. Flowcharts are often used to represent algorithms. ...
Pattern recognition is a field within the area of machine learning. ...
Classification may refer to: Taxonomic classification See also class (philosophy) Statistical classification Security classification Hint: Language use may refer to a taxonomic classification that is used for statistical purposes also as a statistical classification (like International Statistical Classification of Diseases and Related Health Problems). ...
In pattern recognition, features are the individual measurable heuristic properties of the phenomena being observed. ...
In the algorithm, each feature is assigned a dimension to form a multidimensional feature space. A training set of objects with a priori known class are processed by feature extraction and plotted within the multi-dimensional feature space. The offsets in each dimension are referred to as the feature vector. This is the training or learning stage. Because the engine can be retrained to classify various phenomena, pattern recognition is part of machine learning. A priori is a Latin phrase meaning from the former or less literally before experience. In much of the modern Western tradition, the term a priori is considered to mean propositional knowledge that can be had without, or prior to, experience. ...
In computer science, an offset within an array or other data structure object is an integer indicating the distance (displacement) from the beginning of the object up until a given element or point, presumably within the same object. ...
In pattern recognition a feature vector is an n-dimensional vector of features extracted from raw data for further processing. ...
Machine learning is an area of artificial intelligence concerned with the development of techniques which allow computers to learn. More specifically, machine learning is a method for creating computer programs by the analysis of data sets. ...
The testing phase begins with phenomena to be classified (the class not being known a priori) and extracts the same set of features. The geometric distance is computed between the new feature vector and each apriori feature vector from the training set. The shortest distance thus computed is to the nearest neighbor. The a priori class of the nearest neighbor is now assigned to the phenomena to be classified.
Obviously, this algorithm will be more computationally intensive as the size of the training set grows. Many optimizations have been given over the years; these generally seek to reduce the number of distances actually computed. Some optimizations involve partitioning the feature space, and only computing distances within specific nearby volumes.
Other variations of the algorithm include the k-nearest neighbor algorithm where several of the nearest feature vectors are computed, and the classification is made with the highest confidence only if all of the nearest neighbors are of the same class. In pattern recognition, the k-nearest neighbor algorithm is a method for classifying phenomena based upon observable features, similar to the nearest neighbour classification method. ...
Look up Confidence and confidence in Wiktionary, the free dictionary. ...
Nearest neighbor has some strong consistency results. As the amount of data approaches infinity, nearest neighbor is guaranteed to yield an error rate no worse than twice the Bayes error rate (the minimum achievable error rate given the distribution of the data). k-nearest neighbor is guaranteed to approach the Bayes error rate, for some value of k (where k increases as a function of the number of data points). In statistics, a consistent estimator is an estimator that converges in probability to the quantity being estimated as the sample size grows. ...
Different types of nearest neighbor algorithms include:
- Kd-Trees
- Balltrees
- Metric Trees
- Locality-Sensitive Hashing (LSH)
See also
Hondas intelligent humanoid robot Artificial intelligence (AI) is defined as intelligence exhibited by an artificial entity. ...
It has been suggested that Tech mining be merged into this article or section. ...
Machine learning is an area of artificial intelligence concerned with the development of techniques which allow computers to learn. More specifically, machine learning is a method for creating computer programs by the analysis of data sets. ...
Pattern recognition is a field within the area of machine learning. ...
A graph of a bell curve in a normal distribution showing statistics used in educational assessment, comparing various grading methods. ...
Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. ...
References - Belur V. Dasarathy, editor (1991) Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, ISBN 0-8186-8930-7
- Nearest-Neighbor Methods in Learning and Vision, edited by Shakhnarovish, Darrell, and Indyk, The MIT Press, 2005, ISBN 0-262-19547-X
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