Pattern Recognition

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The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform

Josselin Noirel - One of the best experts on this subject based on the ideXlab platform.

Jianchang Mao - One of the best experts on this subject based on the ideXlab platform.

  • Statistical Pattern Recognition: a review
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
    Co-Authors: Anil K. Jain, Robert P W Duin, Jianchang Mao
    Abstract:

    The primary goal of Pattern Recognition is supervised or unsupervised classification. Among the various frameworks in which Pattern Recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a Recognition system requires careful attention to the following issues: definition of Pattern classes, sensing environment, Pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex Patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face Recognition, and cursive handwriting Recognition, require robust and efficient Pattern Recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a Pattern Recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field

Maojianchang - One of the best experts on this subject based on the ideXlab platform.

Jurgen Adamy - One of the best experts on this subject based on the ideXlab platform.

  • sequential Pattern Recognition employing recurrent fuzzy systems
    Fuzzy Sets and Systems, 2004
    Co-Authors: Roland Kempf, Jurgen Adamy
    Abstract:

    Sequential Pattern-Recognition systems check whether data strings, e.g., time series, exhibit certain Pattern primitives in a specified order. As in the case of most other Pattern-Recognition methods, either conventional methods or fuzzy systems may be used here. This paper presents a sequential Pattern-Recognition system employing recurrent fuzzy systems that is employed as a monitoring system on continuous-casting systems in the steel industry worldwide. Taking that application as a starting point, a general method for sequential Pattern Recognition in time series that uses recurrent fuzzy systems is described.

Visakan Kadirkamanathan - One of the best experts on this subject based on the ideXlab platform.