An empirical study on budget-aware online kernel algorithms for streams of graphs.
Giovanni Da San MartinoNicolò NavarinAlessandro SperdutiPublished in: Neurocomputing (2016)
Keyphrases
- online algorithms
- graph theory
- data structure
- computational cost
- computationally efficient
- theoretical analysis
- learning algorithm
- computational complexity
- significant improvement
- worst case
- online learning
- stream clustering
- data sets
- graph isomorphism
- pattern mining
- query processing
- support vector
- clustering algorithm
- machine learning