Search Results - (( basic classification based algorithm ) OR ( data classification learning algorithm ))

Search alternatives:

Refine Results
  1. 1
    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

    Published 2018
    “…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  2. 2
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  3. 3
    Modelling of clinical risk groups (CRGs) classification using FAM

    Modelling of clinical risk groups (CRGs) classification using FAM by Mohd. Asi, Salina, Saad, Puteh

    Published 2006
    “…FAM is a fast learning algorithm and used less epoch training [4]. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  4. 4
    Review of deep convolution neural network in image classification

    Review of deep convolution neural network in image classification by Al-Saffar, Ahmed Ali Mohammed, Tao, Hai, Mohammed, Ahmed Talab

    Published 2017
    “…Then, the research status and development trend of convolution neural network model based on deep learning in image classification are reviewed, which is mainly introduced from the aspects of typical network structure construction, training method and performance. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  5. 5
    The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer

    The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer by Tehseen Mazhar, Inayatul Haq, Allah Ditta, Syed Agha Hassnain Mohsan, Faisal Rehman, Imran Zafar, Jualang Azlan Gansau, Lucky Poh Wah Goh

    Published 2023
    “…The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  6. 6
    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant

    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

    Published 2007
    “…In this research, a vision system algorithm has been developed to identify and locate base of young corn trees based upon robot vision technology, pattern recognition techniques, and knowledge-based decision theory. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  7. 7
    Integrated artificial intelligence-based classification approach for prediction of acute coronary syndrome

    Integrated artificial intelligence-based classification approach for prediction of acute coronary syndrome by Salari, Nader

    Published 2014
    “…Performance evaluation of the proposed model were conducted by comparing 13 well-known classification models based on various commonly used evaluation criteria on seven data sets (ACSEKI data set as well as six data sets taken from the University of California Irvine (UCI) machine-learning repository). …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  8. 8
    Improved normalization and standardization techniques for higher purity in K-means clustering

    Improved normalization and standardization techniques for higher purity in K-means clustering by Dalatu, Paul Inuwa, Fitrianto, Anwar, Mustapha, Aida

    Published 2016
    “…Based on its simplicity, the K-means algorithm has been used in many fields. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  9. 9
    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition

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

    Published 2004
    “…The neural network learns the rough set’s upper and lower approximations as feature extractors simultaneously with classification. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  10. 10
    Raspberry Pi-Based Finger Vein Recognition System Using PCANet

    Raspberry Pi-Based Finger Vein Recognition System Using PCANet by Quek, Ee Wen

    Published 2018
    “…Factors which impact PCANet are studied to identify the limitations of PCANet. For classification, k-Nearest Neighbours (kNN) with Euclidean distance algorithm is implemented. …”
    Get full text
    Get full text
    Monograph
    Save to List
    Saved in:
  11. 11
    Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning

    Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning by Solihin M.I., Yanto, Hayder G., Maarif H.A.-Q.

    Published 2024
    Conference Paper
    Save to List
    Saved in:
  12. 12
    Identification model for hearing loss symptoms using machine learning techniques

    Identification model for hearing loss symptoms using machine learning techniques by Nasiru Garba Noma

    Published 2014
    “…The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  13. 13
    Text-based emotion prediction system using machine learning approach

    Text-based emotion prediction system using machine learning approach by Ahmad Fakhri, Ab. Nasir, Eng, Seok Nee, Chun, Sern Choong, Ahmad Shahrizan, Abdul Ghani, Anwar, P. P. Abdul Majeed, Asrul, Adam, Mhd, Furqan

    Published 2020
    “…Therefore, four supervised machine learning classification algorithms such as Multinomial Naïve Bayes, Support Vector Machine, Decision Trees, and kNearest Neighbors were investigated. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  14. 14
    Mean of correlation method for optimization of affective states detection in children

    Mean of correlation method for optimization of affective states detection in children by Rusli, Nazreen, Sidek, Shahrul Na'im, Md Yusuf, Hazlina, Ishak, Nor Izzati

    Published 2018
    “…At the moment, most of the studies on classification of affective states for children focus on visual observations and physiological cues, where all data collection for measuring physiological signals are contact-based and invasive. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  15. 15
    Case Slicing Technique for Feature Selection

    Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

    Published 2004
    “…One of the problems addressed by machine learning is data classification. Finding a good classification algorithm is an important component of many data mining projects. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  16. 16
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  17. 17
    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…In data mining, classification learning is broadly categorized into two categories; supervised and unsupervised. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  18. 18
    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

    Published 2019
    “…Feature selection and classification are widely utilized for data analysis. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  19. 19
    Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach

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

    Published 2009
    “…The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  20. 20
    New Learning Models for Generating Classification Rules Based on Rough Set Approach

    New Learning Models for Generating Classification Rules Based on Rough Set Approach by Al Shalabi, Luai Abdel Lateef

    Published 2000
    “…Two different models for learning in data sets were proposed based on two different reduction algorithms. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:

Search Tools: