code matlab algoritm pso svm
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i want a project with full description of all the principles of managements  plus an example of a big firm who is undertaking some of the principles 

kindly,
SEND ME THE CONTENT FOR THE SAME 

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AYUSH
(STUDENT)
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#2

Today, feature selection is an active research into automatic learning. The main idea of feature selection is to select a subset of available features, eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. Particle swarm optimization (PSO) was combined with the support vector machine (SVM) to improve classification accuracy and select a subset of outgoing characteristic. This optimization mechanism with discrete PSO and continuous PSO combination simultaneously selects a subset of outgoing characteristic and the melodies support vector machine parameters. In this algorithm, a new local search based on reinforcement learning is used to obtain an optimal subset of characteristics. Numerical results and statistical analysis show that the proposed method works significantly better than the other methods in terms of prediction accuracy with lower subset of features in low and high dimensional databases.


A Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA) (both augmented with Support Vector Machines SVM) for the classification of high-dimensional microarray data. Both algorithms are used to find small samples of informative genes among thousands of them. An SVM classifier with 10-fold cross validation is applied to validate and evaluate the solutions provided. A first contribution is to demonstrate that PSOSV M is able to find interesting genes and provide a competitive performance classification. Specifically, a new version of PSO, called Geometric PSO, is empirically evaluated for the first time in this paper using a binary representation in the Hamming space
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