Machine level classification using support vector machine

Abstract

In the current era, content based image retrieval based on pattern recognition and classification using machine learning paradigm is an innovative way. In order to retrieve high resolution security for satellite images Support Vector Machine (SVM) a machine learning paradigm is helpful for learning process and for pattern recognition and classification; ensemble methods give better machine learning results. In this paper, SVM based on random subspace and boosting ensemble learning is proposed for very high resolution security for satellite image retrieval and secure from the Mobile Ad-hoc Network (MANET). To taught SVM ensemble model is use to recognize the images that almost all similar informative for dynamic learning. Bias-weighting systems implement to direct the ensemble model to pay a lot of attention on the positive illustration than the negative ones. The UC Merced security for satellite image from dataset is used for experimental work. Accuracy and error rate are found to be precise. The tentative effects illustrate that the proposed model derived enhanced retrieval accurateness at the optimum level as well as significantly more effective and secure data from the MANET. The comparisons for the existing approaches are evaluated by using precision and recall measurements.