Search Results - ((pattern classification) OR (based classification)) (problems OR problem )

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  1. 1

    Multilevel learning in Kohonen SOM network for classification problems by Mohd. Yusof, Norfadzila

    Published 2006
    “…Classification is the procedure of recognizing classes of patterns that occur in the environment and assigning each pattern to its relevant class. …”
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    A derivative-free optimization method for solving classification problem by Shabanzadeh, Parvaneh, Abu Hassan, Malik, Leong, Wah June

    Published 2010
    “…There is a training set for each class. Those problems occur in a wide range of human activity. One of the most promising ways to data classification is based on methods of mathematical optimization. …”
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  4. 4

    A survey of fuzzy min max neural networks for pattern classification: variants and applications by Al Sayaydeh, Osama Nayel, Mohammed, Mohammed Falah, Lim, Chee Peng

    Published 2018
    “…This division facilitates understanding of the improvements on the original FMM model, as well as enables identification of the limitations that still exist in various FMM-based models. We also summarize the use of FMM and its variants in solving different benchmark and real-world pattern classification problems. …”
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  5. 5

    Text classification using modified multi class association rule by Kamaruddin, Siti Sakira, Yusof, Yuhanis, Husni, Husniza, Al Refai, Mohammad Hayel

    Published 2016
    “…This paper presents text classification using a modified Multi Class Association Rule Method.The method is based on Associative Classification which combines classification with association rule discovery. …”
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  6. 6

    Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach by Moheb Pour, Majid Reza

    Published 2009
    “…Pattern recognition/classification has received a considerable attention in engineering fields. …”
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    Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

    Published 2011
    “…The focus of this thesis is on solvingclustering and classification problems. Specifically, we will focus on new optimization methods for solving clustering and classification problems. …”
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  9. 9

    A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification by Quteishat, A., Lim, C.P., Tan, K.S.

    Published 2010
    “…In this paper, a two-stage pattern classification and rule extraction system is proposed. …”
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  10. 10

    Flexible enhanced fuzzy min–max neural network model for pattern classification problems by Al-Hroob, Essam Muslem Harb

    Published 2020
    “…The results demonstrate the efficiency of FEFMM in handling pattern classification problems and providing a superior performance of classification accuracy as compared to the other network structures from the same variants such as EFMM, FMM variants and also non-FMM related models. …”
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  11. 11

    Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression by Yap , Keem Siah

    Published 2010
    “…This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. …”
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  12. 12

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…Data level based methods are meant to solve the imbalanced classification problem based on the idea of making both classes equal in number. …”
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  13. 13

    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

    Published 2004
    “…The extraction network extracts detectors that represent pattern’s classes to be supplied to the classification network. …”
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  14. 14

    Classification for large number of variables with two imbalanced groups by Ahmad Hakiim, Jamaluddin

    Published 2020
    “…Several approaches have been devoted to study such problems using linear and non-linear classification rules, but limited to group imbalance rather than the combination of both problems. …”
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  15. 15

    A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification by Alhroob, Essam, Mohammed, Mohammed Falah, Lim, Chee Peng, Tao, Hai

    Published 2019
    “…FMM is considered one of the most useful neural networks for pattern classification. This paper aims to 1) analyze the FMM neural network in terms of its impact in addressing pattern classification problems; 2) examine models that are proposed based on the original FMM model (i.e., existing FMM-based variants); 3) identify the challenges associated with FMM and its variants, and; 4) discuss future trends and make recommendations for improvement. …”
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    Grid base classifier in comparison to nonparametric methods in multiclass classification by Moheb Pour, Majid Reza, Jantan, Adznan, Saripan, M. Iqbal

    Published 2010
    “…The method was developed based on the grid structure which was done to create a powerful method for classification. …”
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    Rough Set Discretize Classification of Intrusion Detection System by Noor Suhana, Sulaiman, Rohani, Abu Bakar

    Published 2016
    “…Many pattern classification tasks confront with the problem that may have a very high dimensional feature space like in Intrusion Detection System (IDS) data. …”
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    Evaluation and optimization of frequent association rule based classification by Izwan Nizal Mohd Shaharanee, Jastini Jamil

    Published 2014
    “…Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. …”
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  20. 20

    Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification by Navan, Farhad Pourpanah

    Published 2015
    “…The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks. …”
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