Comparison of maximum likelihood, support vector machines, and random forest techniques in satellite images classification

Classification of Images Using Support Vector Machines

Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the...

Support Vector Machines et Classification d’ Images

Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms

Land cover change assessment is one of the main applications of remote sensed data. A number of pixel based classification algorithmshave beendevelopedover thepast years for the analysis of remotely sensed data. Themost notable include themaximum likelihood classifier (MLC), support vectormachines (SVMs) and thedecision trees (DTs). TheDTs in particular offer advantages not providedbyother appr...

Comparative Prediction Performance with Support Vector Machine and Random Forest Classification TechniquesComparative Prediction Performance with Support Vector Machine and Random Forest Classification Techniques

Machine learning with classification can effectively be applied for many applications, especially those with complex measurements. Therefore classification technique can be used for prediction of diseases like cancer, liver disorders and heart disease etc which involve complex measurements. This is part of growing demand and much interesting towards predictive diagnosis. It has also been establ...

comparison of the amount of debris extruded apically in two rotary techniques: flexmaster and m2

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