Results 261 to 270 of about 735,517 (309)

A feature fusion method for feature extraction

SPIE Proceedings, 2012
The automatic target recognition based on image fusion refers to the fusion process using the target images provided by a variety of sensors, so as to improve the recognition accuracy and robustness and to obtain better recognition performance.
Dejun Tang, Weishi Zhang, Xiaolu Qu, Dujuan Wang   +3 more
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Redundancy in Feature Extraction

IEEE Transactions on Computers, 1971
Given two random variables X and Y, a definition is offered that gives a condition for Y to be redundant with respect to X. It is shown that if such redundancy exists, then observations on Y, i.e., pattern vector elements related to Y, can be eliminated without increasing the classification error.
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Pronunciation Feature Extraction

2005
Automatic pronunciation scoring makes novel applications for computer assisted language learning possible. In this paper we concentrate on the feature extraction. A relatively large feature vector with 28 sentence- and 33 word-level features has been designed.
Christian Hacker, Tobias Cincarek, Rainer Gruhn, Stefan Steidl, Elmar Nöth, Heinrich Niemann   +5 more
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Feature Extraction Through LOCOCODE

Neural Computation, 1999
Low-complexity coding and decoding (LOCOCODE) is a novel approach to sensory coding and unsupervised learning. Unlike previous methods, it explicitly takes into account the information-theoretic complexity of the code generator. It computes lococodes that convey information about the input data and can be computed and decoded by low-complexity ...
Hochreiter, Sepp, Schmidhuber, Jürgen
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On Feature Extraction via Kernels

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008
Using the kernel trick idea and the kernels-as-features idea, we can construct two kinds of nonlinear feature spaces, where linear feature extraction algorithms can be employed to extract nonlinear features. In this correspondence, we study the relationship between the two kernel ideas applied to certain feature extraction algorithms such as linear ...
Cheng Yang, Liwei Wang 0001, Jufu Feng
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Feature extraction algorithms

Pattern Recognition, 1971
Abstract This paper describes methods for extracting pattern-synthesizing features. A set of patterns is expressed as a Boolean matrix, allowing the problem of feature extraction to be viewed as one of factoring this matrix. Feature extraction methods based on matrix factorization and pattern intersection are presented. Attribute inclusion is defined
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A Binary Feature Extraction Technique

IEEE Transactions on Computers, 1974
Numerous schemes are available for feature selection in a pattern recognition problem, but the feature extraction process is largely intuitive. A sequential feature extraction scheme is proposed for binary features. A decision function, which is linear and near optimal, is developed concurrently with each feature.
K. S. Grewal, J. D. Patterson
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Software feature extraction using infrequent feature extraction

2016 6th International Annual Engineering Seminar (InAES), 2016
Evolution and maintenance processes are important but time consuming and expensive. It is very important to make the processes effective and efficient. A software developer can use resource like user opinion data to get information, such as user request, bug report, and user experience.
Divi Galih Prasetyo Putri, Daniel Oranova Siahaan   +1 more
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Feature extraction in classification

2013
Feature extraction, or dimensionality reduction, is an essential part of many machine learning applications. The necessity for feature extraction stems from the curse of dimensionality and the high computational cost of manipulating high-dimensional data. In this thesis we focus on feature extraction for classification.
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Logical Networks for Feature Extraction

IEEE Transactions on Systems, Man, and Cybernetics, 1971
The extraction of features for the purpose of reconstituting a pattern set may be particularly useful in those cases where a large number of patterns can be decomposed into a relatively small set of sub-patterns. By the representation of pattern and feature sets as matrices, the concept of feature determination has been extended to multilevel features ...
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