Land Use and Cover Mapping Using SVM and MLC Classifiers: A Case Study of Aurangabad City, Maharashtra, India.
Abdulla A. OmeerRatnadeep R. DeshmukhRohit S. GuptaJaypalsing N. KaytePublished in: RTIP2R (3) (2018)
Keyphrases
- support vector
- svm classifier
- support vector machine classifiers
- urban areas
- training data
- support vector machine
- support vector machine svm
- training set
- feature selection
- decision boundary
- classification algorithm
- classification method
- linear support vector machines
- improves the classification accuracy
- rule based classifier
- fold cross validation
- text classifiers
- spatial analysis
- train a support vector machine
- support vectors
- classifier training
- small sample
- high classification accuracy
- feature ranking
- linear classifiers
- ensemble classifier
- highest accuracy
- discriminative classifiers
- linear svm
- svm classification
- bayes classifier
- kernel support vector machines
- knn
- hybrid algorithms
- bayesian classifiers
- training samples
- multi label
- generalization ability
- training examples
- kernel function
- input features
- unbalanced data
- multi class
- classification accuracy
- spatial data
- geographic information systems
- imbalanced data
- rbf kernel
- kernel methods
- hyperplane
- image classification
- developing countries
- majority voting
- spatial distribution
- remote sensing
- feature space
- decision trees
- kernel svms
- large margin classifiers
- class labels
- training set size
- decision forest
- remote sensing images
- supervised classification
- ls svm
- machine learning
- text categorization
- multi class classification