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1
Support directional shifting vector: A direction based machine learning classifier
Published 2021“…There exist several types of classification algorithms, and these are based on various bases. …”
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2
An improvement algoithm for Iris classification by using Linear Support Vector Machine (LSVM), k-Nearest Neighbours (k-NN) and Random Nearest Neighbous (RNN) / Ahmad Haadzal Kamar...
Published 2019“…In machine learning, there are three type of learning branch that can used in classification procedures for data mining. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…The selected features are then fed into a linear support vector machine (SVM) for classification. …”
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4
Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm
Published 2010“…In this work, we integrate artificial immune algorithm with non-linear classification of pattern recognition and machine learning methods to solve the problem of intrusion detection in network systems. …”
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5
A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The proposed algorithms have been tested on a variety of datasets from the UCI machine learning repository. …”
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6
An improved algorithm for iris classification by using support vector machine and binary random machine learning
Published 2018“…In machine learning, there are three type of learning branch that can used in classification procedures for data mining. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. …”
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8
Evaluating machine learning algorithms for sentiment analysis: a comparative study to support data-driven decision making
Published 2025“…The study employs data from the IMDb 500k movie reviews dataset, utilizing machine learning techniques for sentiment classification. Specifically, the selected algorithms—Bernoulli Naive Bayes, Linear Support Vector Machines, and Logistic Regression—are employed to train the dataset. …”
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Machine learning classifications of multiple organ failures in a malaysian intensive care unit
Published 2024“…This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. …”
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10
An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2013“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2012“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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12
An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2012“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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13
Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit
Published 2025“…This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. …”
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14
Kernel and multi-class classifiers for multi-floor wlan localisation
Published 2016“…Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. …”
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15
Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis
Published 2023“…Then, several feature selections and machine learning algorithms were also evaluated to produce a super lightweight and robust landmark recognition model. …”
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Evaluating integrated weight linear method to class imbalanced learning in video data
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Characterisation of pineapple cultivars under different storage conditions using infrared thermal imaging coupled with machine learning algorithms
Published 2022“…Several types of machine learning algorithms were compared, including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and naïve Bayes, to obtain the best performance for the classification of pineapple cultivars. …”
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Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System
Published 2011“…The proposed algorithms have been tested on a variety of datasets from the UCI machine learning repository. …”
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19
Classification Of Hand Movements Based On Discrete Wavelet Transform And Enhanced Feature Extraction
Published 2019“…The extracted features are then fed into the machine learning algorithm for classification process. Four popular machine learning algorithms include k-nearest neighbor (KNN), linear discriminate analysis (LDA), Naïve Bayes (NB) and support vector machine (SVM) are used in evaluation. …”
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An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification
Published 2024“…This work proposes using machine learning algorithms and electronic nose technology to recognise and forecast the contamination in leftover cooked food. …”
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