Search Results - (( initial solution method algorithm ) OR ( parameter classification _ algorithm ))
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Finite impulse response optimizers for solving optimization problems
Published 2019“…Simulated Kalman filter (SKF) algorithm is one of the algorithms under this classification. …”
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Finite impulse response optimizers for solving optimization problems
Published 2019“…Simulated Kalman filter (SKF) algorithm is one of the algorithms under this classification. …”
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Digital economy tax compliance model in Malaysia using machine learning approach
Published 2021“…Based on the validation of training data with the presence of seven single classifier algorithms, three performance improvements have been established through ensemble classification, namely wrapper, boosting, and voting methods, and two techniques involving grid search and evolution parameters. …”
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Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. …”
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Intelligent classification algorithms in enhancing the performance of support vector machine
Published 2019“…Common methods associated in tuning SVM parameters will discretize the continuous value of these parameters which will result in low classification performance. …”
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Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…In addition, experiments prove that incremental genetic-based clustering ensemble algorithm speed up to converge into an optimal clustering solution, where pattern ensemble learning method and the cluster partitions produced by the threshold fuzzy c-means clustering algorithm are employed as recombination operator and initial population, respectively.…”
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Multistage optimal homotopy asymptotic method for solving initial-value problems
Published 2016“…In this paper, a new approximate analytical algorithm namely multistage optimal homotopy asymptotic method (MOHAM) is presented for the first time to obtain approximate analytical solutions for linear, nonlinear and system of initial value problems (IVPs).This algorithm depends on the standard optimal homotopy asymptotic method (OHAM), in which it is treated as an algorithm in a sequence of subinterval. …”
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Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Published 2013“…Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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Combining approximation algorithm with genetic algorithm at the initial population for NP-complete problem
Published 2018“…In Genetic Algorithm (GA), the prevalent approach to population initialization are heuristics and randomization. …”
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Initialization Methods For Conventional Fuzzy C-Means And Its Application Towards Colour Image Segmentation
Published 2011“…Due to its capability in providing a particularly promising solution to clustering problems, the conventional Fuzzy C-Mean (FCM) algorithm is widely used as a segmentation method. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…The third stage is to classify individual jammers according to the specific pattern and characteristics design as defined in jamming identification and classification parameters. It involves development of Max-Min Rule-Based Classification Algorithm. …”
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Adaptive parameter control strategy for ant-miner classification algorithm
Published 2020“…This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-Ant Miner. …”
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Comparison between Newton’s Method and a new Scaling Newton Method / Ramizah Baharuddin
Published 2021“…Newton's Method also called the Newton-Raphson method is a recursive algorithm for approximating the root of a differentiable function. …”
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Optimizing support vector machine parameters using continuous ant colony optimization
Published 2012“…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous parameters, there is a need to discretize the continuous value into a discrete value.This discretization process results in loss of some information and, hence, affects the classification accuracy and seek time.This study proposes an algorithm to optimize Support Vector Machine parameters using continuous Ant Colony Optimization without the need to discretize continuous values for Support Vector Machine parameters.Seven datasets from UCI were used to evaluate the performance of the proposed hybrid algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques.Experimental results of the proposed algorithm also show promising performance in terms of computational speed.…”
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Shooting method with root finding to solve boundary value problems / Aryani Rima Matakim
Published 2025“…This method involves guessing initial conditions and iteratively adjusting them until the solution satisfies the boundary conditions at the other end of the domain. …”
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Gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network
Published 2020“…This was to obtain a good combination of parameters in order to produce a better gender classification. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. …”
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Integrated ACOR/IACOMV-R-SVM Algorithm
Published 2017“…The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. …”
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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
Published 2024“…This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. …”
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