Results 261 to 270 of about 4,361,783 (305)

Pattern classification using teurons

[1990] Proceedings. 10th International Conference on Pattern Recognition, 2002
Neural networks consist of simple elements capable of summation and thresholding. The authors define a more general element, the task-oriented neuron or teuron, which can compute higher-order functions. They further define teuron networks and show two such networks that can be used as content addressable memories and as pattern classifiers.
Moshe Sipper, Hezy Yeshurun
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Frequency domain pattern classification

Information Sciences, 1973
Abstract This paper considers the problem of pattern classification in the frequency domain. The test statistic depends on the spectral density of each pattern class which may or may not be known. When the spectral density of the unknown input pattern to be classified is not known then the technique of time series analysis is applied to estimate its ...
Rajat K. Saha, Someshwar C. Gupta
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Pattern Classification of Phylogeny Signals

Statistical Applications in Genetics and Molecular Biology, 2008
In this paper we propose the minimum entropy clustering (MEC) method for clustering genes based on their phylogenetic signals. This entropy based method will cluster two genes together when their concatenation can decrease the entropy. An integral feature of MEC is that it chooses the number of clusters automatically, which is a major advantage over ...
Xiaofei, Shi, Hong, Gu, Chris, Field
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Pattern recognition in biologic classification

Pattern Recognition, 1968
Abstract In lieu of any knowledge of an a priori classification, biologic objects of the environment (viz., higher plants and animals) are independently classified by men and some animals with a remarkable uniformity which can be interpreted to reflect evolutionary relationships between the classified objects.
R. A. Dunn, R. A. Davidson
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The Classification Stable Analysis Pattern

2007 IEEE International Conference on Information Reuse and Integration, 2007
The main goal of this paper is to extract and document the core knowledge of the classification technique, as an analysis pattern that is usable in many different applications, where classification concept is required, rather than repeatedly building the concept from its scratch.
M. E. Fayad, Somenath Das
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Mining sequential patterns for classification

Knowledge and Information Systems, 2015
While a number of efficient sequential pattern mining algorithms were developed over the years, they can still take a long time and produce a huge number of patterns, many of which are redundant. These properties are especially frustrating when the goal of pattern mining is to find patterns for use as features in classification problems. In this paper,
Dmitriy Fradkin, Fabian Mörchen
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Interpretation and classification of fringe patterns

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. IV. Conference D: Architectures for Vision and Pattern Recognition,, 1992
Abstract A procedure for interpretation and classification of digital fringes, including design of quantitative features and methods of feature extraction, is presented. Totally, 14 parameters, related to the geometrical shape and the physical meaning of fringes, are proposed. These parameters can be used either for a microscopic description of the
Huang Zhi, Rolf B. Johansson
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Classification of binary random patterns

IEEE Transactions on Information Theory, 1965
In various pattern-recognition problems such as classification of photographic data, preprocessing operations result in a two-dimensional array of binary random variables. An optimal recipe for classifying such patterns is described. It combines the use of an orthonormal expansion for the logarithm of probability functions, with the generation of a ...
Kenneth Abend, Thomas J. Harley, Laveen N. Kanal   +2 more
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Evolving Hypernetworks for Pattern Classification

2007 IEEE Congress on Evolutionary Computation, 2007
Hypernetworks consist of a large number of hyperedges that represent higher-order features sampled from training patterns. Evolutionary algorithms have been used as a method for evolving hypernetworks. The order of a hyperedge is defined as the number of feature variables in the hyperedge and it is an important parameter of the hypernetwork model ...
Joo-Kyung Kim, Byoung-Tak Zhang
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Fingerprint Pattern Identification and Classification

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2018
Fingerprint as a unique feature of each person can be divided into different types, in this paper, we identify real fingerprints pattern and classify them with convolutional neural networks(CNN). The traditional fingerprint pattern classification method is only classified by artificially defined features, which is supervised learning, and requires much
Xiaomeng Guo, Fan Wu 0016, Xiaoyong Tang
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