Activity classification at a higher level: what to do after the classifier does its best?
Rabih YounesThomas L. MartinMark T. JonesPublished in: ISWC (2015)
Keyphrases
- higher level
- classification scheme
- classification method
- support vector
- decision trees
- classification process
- classification algorithm
- svm classifier
- feature selection
- final classification
- classification rate
- multiple classifiers
- supervised classification
- training set
- nearest neighbor classifier
- classification models
- classification schemes
- class labels
- learning vector quantization
- higher classification accuracy
- feature space
- classifier combination
- image classification
- high classification accuracy
- training samples
- probabilistic classifiers
- multi category
- feature extraction and classification
- decision boundary
- support vector machine
- feature set
- support vector machine svm
- accurate classification
- training data
- discriminative classifiers
- machine learning
- binary classifiers
- supervised learning
- nearest neighbor rule
- low level
- combining classifiers
- bayesian classifier
- feature vectors
- multiclass classification
- classification accuracy
- fuzzy classifier
- learning algorithm
- correctly classified
- unseen data
- robust classification
- k nearest neighbour
- hierarchical classification
- fold cross validation
- feature extraction
- naive bayes classifier
- multiple classifier systems
- naive bayes
- pattern classification
- extracted features
- rule based classifier
- pattern recognition
- text classification
- roc curve
- lower level
- multiple kernel learning
- accurate classifiers
- individual classifiers