Thangairulappan Kathirvalavakumar
Artificial Intelligence · 2011–2013
A researcher in the Science corpus: 4 papers; trusts 19 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 19 citations into it; #6,044 of 35,345 by trust here, #14,718 by citations
- Computational Theory and Mathematics — 6 citations into it; #1,830 of 12,182 by trust here, #4,365 by citations
- Efficiency & Compression — 6 citations into it; #1,183 of 1,264 by trust here, #1,225 by citations
Whom Thangairulappan Kathirvalavakumar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Rudy Setiono
- Mark W. Craven
- Eduardo R. Hruschka
- Humar Kahramanli
- Terence Anthony Etchells
- Gethsiyal Augasta M
- Robert Andrews
- Jude W. Shavlik
- G. Huang
- Nelson Francisco Favilla Ebecken
Who trusts Thangairulappan Kathirvalavakumar overall
- Leilani H. Gilpin
- Tameru Hailesilassie
- Vaishak Belle
- Gethsiyal Augasta M
- David Bau
- Bo Li
- Gesina Schwalbe
- Nadia Burkart
- Ioannis Papantonis
- Jan Ruben Zilke
Papers from Thangairulappan Kathirvalavakumar's perspective
Ordered by how strongly the researchers Thangairulappan Kathirvalavakumar trusts endorse each paper. Same corpus, a different order for every researcher.
- Using Sampling and Queries to Extract Rules from Trained Neural Networks
- Survey and critique of techniques for extracting rules from trained artificial neural networks
- FERNN: An Algorithm for Fast Extraction of Rules from Neural Networks
- The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks
- Understanding Neural Networks via Rule Extraction
- Introduction to artificial neural systems
- Extracting rules from multilayer perceptrons in classification problems: A clustering-based approach
- Neural network explanation using inversion
- Extracting -of- Rules from Trained Neural Networks
- Extracting refined rules from knowledge-based neural networks
Their papers
- Pruning algorithms of neural networks — a comparative study (2013) — 130 citations
- Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems (2012) — 180 citations
- Rule extraction from neural networks — A comparative study (2012) — 55 citations
- Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems (2011)