Many methods of the mlpack machine learning library (such as nearest neighbor search, range search and others) are based on dual-tree algorithms which deal with data organized into space trees in order to reduce the number of queries. All these methods are designed in a tree-independent manner. The main goal of this project is to extend the mlpack's list of tree types by implementing R+ trees, Hilbert R trees, vantage point trees, random projection trees and UB trees for the purpose of using them in mlpack's dual-tree algorithms.

Student

lozhnikov

Mentors

  • Ryan Curtin
close

2016