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  • Discriminative learning and informative learning in pattern recognition

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    55733_1.pdf (342.4Kb)
    Author(s)
    Wang, XC
    Paliwal, KK
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2002
    Metadata
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    Abstract
    In pattern recognition, the goal of classification can be achieved from two different types of learning strategy-discriminative teaming and informative learning. Discriminative learning focuses on extracting the discriminative information between classes. Informative learning emphasizes the learning of the class information such as class densities. We review major discriminative learning methods, namely, principal component analysis (PCA), linear discriminant analysis (LDA), minimum classification error (MCE) training algorithm and support vector machine (SVM) and one informative learning method-Gaussian mixture models (GMM). ...
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    In pattern recognition, the goal of classification can be achieved from two different types of learning strategy-discriminative teaming and informative learning. Discriminative learning focuses on extracting the discriminative information between classes. Informative learning emphasizes the learning of the class information such as class densities. We review major discriminative learning methods, namely, principal component analysis (PCA), linear discriminant analysis (LDA), minimum classification error (MCE) training algorithm and support vector machine (SVM) and one informative learning method-Gaussian mixture models (GMM). We also discuss the combination of the two types of learning and give the corresponding experiments results.
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    Conference Title
    ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING
    Volume
    2
    Publisher URI
    http://ieeexplore.ieee.org/servlet/opac?punumber=8534
    DOI
    https://doi.org/10.1109/ICONIP.2002.1198182
    Copyright Statement
    © 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Publication URI
    http://hdl.handle.net/10072/24598
    Collection
    • Conference outputs

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