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Dual-Tree Algorithms.pdf

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Preview of Dual-Tree Algorithms
🔗 Source: proceedings.mlr.press
📊 Size: 351 KB
👤 Author: Ryan R. Curtin, William B. March, Parikshit Ram, David V. Anderson, Alexander G. Gray, Charles L. Is
⬇️ Downloads: 104

Summary

Dual-tree algorithms are a class of branch-and-bound algorithms used in machine learning, but developing them for different trees and problems is complex. A four-part logical split is introduced: tree, traversal, point-to-point base case, and pruning rule, allowing for a meta-algorithm to develop dual-tree algorithms in a tree-independent manner. This enables easy extension to new types of trees and provides representations for five common algorithms, including a novel, tighter pruning bound for k-nearest neighbor search. The meta-algorithm also allows for straightforward extensions to massively parallel settings.

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Description

Dual-tree algorithms are a class of branch-and-bound algorithms used in machine learning, but developing them for different trees and problems is complex.

Technical Information

  • File Format: PDF
  • File Size: 351 KB
  • Pages: 9
  • Language: EN
  • Author: Ryan R. Curtin, William B. March, Parikshit Ram, David V. Anderson, Alexander G. Gray, Charles L. Is
  • Total Downloads: 104
  • Last Updated: 3 hours ago

Document Overview

This PDF document about Dual-Tree Algorithms provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Dual-Tree Algorithms.

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