Search Results - (( parameter classification _ algorithm ) OR ( pattern classification problems algorithm ))
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1
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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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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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…It involves development of Max-Min Rule-Based Classification Algorithm. The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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4
Optimizing support vector machine parameters using continuous ant colony optimization
Published 2012“…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 may be done experimentally through time consuming human experience.To overcome this difficulty, an approach such as Ant Colony Optimization can tune Support Vector Machine parameters.Ant Colony Optimization originally deals with discrete optimization problems. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.…”
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Backpropagation algorithm for classification problem: academic performance prediction model for UiTM Melaka Mengubah Destini Anak Bangsa (MDAB) program. / Fadhlina Izzah Saman, Nur...
Published 2012“…Artificial neural networks (ANN) has become one of the artificial intelligent techniques that has many successful examples when applied to classification problem such as doing pattern recognition and prediction. …”
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Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
Published 2021“…To solving pattern classification problem, the optimization deep learning architecture and parameter by using four convolution layers is set up to classify the three pathological signs; HEM, MA and exudate. …”
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An improved multiple classifier combination scheme for pattern classification
Published 2015“…Combining multiple classifiers are considered as a new direction in the pattern recognition to improve classification performance. …”
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On equivalence of FIS and ELM for interpretable rule-based knowledge representation
Published 2023Article -
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…To further augment the ARTMAP's pattern classification ability, multiple ARTMAPs were optimized via genetic algorithm and assembled into a classifier ensemble. …”
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Case study : an effect of noise in character recognition system using neural network
Published 2003“…Neural networks are useful tools for solving many type of problems. These problems may be characterized as mapping(including pattern association and pattern classification), clustering and constrained optimization. …”
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Effect of displacement vector in the direction of arrival estimation / Nor Syaliza Talha
Published 2007“…The objective is to find the optimum value of element spacing where it will give the best DOA estimation of signal impinging on a uniform linear array (ULA). The algorithms used in detecting the DOA are the Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational invariance Techniques (ESPRIT). …”
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Effect of displacement vector in the direction of arrival estimation / Nor Syaliza Talha
“…The objective is to find the optimum value of element spacing where it will give the best DOA estimation of signal impinging on a uniform linear array (ULA). The algorithms used in detecting the DOA arc the Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational invariancc Techniques (ESPRIT). …”
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Integrated Features by Administering the Support Vector Machine of Translational Initiations Sites in Alternative Polymorphic Context
Published 2012“…Many algorithms and methods have been proposed for classification problems in bioinformatics. …”
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A framework of modified adaptive neuro-fuzzy inference engine
Published 2012“…The performance of MANFIE was compared with existing methods in a diversity of practical benchmark applications such as pattern classifications, time series predictions, modeling with inverse learning control and mobile robot navigation. …”
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Finding an effective classification technique to develop a software team composition model
Published 2018“…Thus, this study aims to (1) discover an effective classification technique to solve the problem and (2) develop a model for composition of the software development team. …”
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Finding an effective classification technique to develop a software team composition model
Published 2018“…Thus, this study aims to (1) discover an effective classification technique to solve the problem and (2) develop a model for composition of the software development team. …”
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Spiking Neural Network For Energy Efficient Learning And Recognition
Published 2020“…The use of applications consumes energy and hard to perform through standard programmed algorithms. Spiking neural networks have emerged that achieve favourable advantages in terms of energy and time efficiency by using spikes for computation and communication as well as solving different problems such as pattern classification and image processing. …”
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Non-fiducial based ECG biometric authentication using one-class support vector machine
Published 2017“…Identity recognition encounters with several problems especially in feature extraction and pattern classification. …”
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