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Pattern recognition focuses more on the signal and also takes acquisition and signal processing into consideration. It originated in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition.
Pattern recognition requires repetition of experience. Semantic memory, which is used implicitly and subconsciously, is the main type of memory involved in recognition. [2] Pattern recognition is crucial not only to humans, but also to other animals. Even koalas, which possess less-developed thinking abilities, use pattern recognition to find ...
Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.
In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition , the match usually has to be exact: "either it will or will not be a match."
A probabilistic neural network (PNN) [1] is a feedforward neural network, which is widely used in classification and pattern recognition problems.In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function.
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written ...
Neural networks help make soft computing extraordinarily flexible and capable of handling high-level problems. In soft computing, neural networks aid in pattern recognition, predictive modeling, and data analysis. They are also used in image recognition, natural language processing, speech recognition, and systems. [3] [10]
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values ...