Marko Robnik-Sikonja

Artificial Intelligence · 2003–2017

A researcher in the Science corpus: 6 papers; trusts 25 researchers and is trusted by 107 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #2,249 of 35,345 by trust here, #6,569 by citations
• Safety, Robustness & Interpretability — 13 citations into it; #244 of 2,251 by trust here, #1,220 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #3,725 of 21,575 by trust here, #9,563 by citations
• Evaluation & Benchmarks — 8 citations into it
• Game & Decision Theory — 6 citations into it
• Model Architectures — 6 citations into it
• Signal Processing — 6 citations into it
• Algorithms & Complexity — 5 citations into it

Whom Marko Robnik-Sikonja trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Igor Kononenko
2. J. Ross Quinlan
3. Lloyd S. Shapley
4. Mark W. Craven
5. Henrik Jacobsson
6. Geoffrey G. Towell
7. Rudy Setiono
8. Jude W. Shavlik
9. Erik Strumbelj
10. Jerome H. Friedman

Who trusts Marko Robnik-Sikonja overall

1. Erik Strumbelj
2. Zheng Zhao
3. Huan Liu
4. Igor Kononenko
5. Luisa M. Zintgraf
6. Lucy R. Chai
7. Giulia Vilone
8. Lei Yu
9. Nadia Burkart
10. Mariusz Bojarski

Papers from Marko Robnik-Sikonja's perspective

Ordered by how strongly the researchers Marko Robnik-Sikonja trusts endorse each paper. Same corpus, a different order for every researcher.

1. Random Forests
2. Neural Networks for Pattern Recognition
3. The Nature of Statistical Learning Theory
4. Extracting Refined Rules from Knowledge-Based Neural Networks
5. Induction of decision trees
6. Finite Mixture Models
7. C4.5: Programs for Machine Learning
8. Extracting refined rules from knowledge-based neural networks
9. Using Sampling and Queries to Extract Rules from Trained Neural Networks
10. Extracting Tree-Structured Representations of Trained Networks

Their papers

• Explaining Classifications For Individual Instances (2008) — 293 citations
• Theoretical and Empirical Analysis of ReliefF and RReliefF (2003) — 2,984 citations
• Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF (2004) — 770 citations
• Explaining machine learning models in sales predictions (2017) — 153 citations
• Explanation and Reliability of Individual Predictions (2013) — 12 citations
• Efficiently Explaining Decisions of Probabilistic RBF Classification Networks (2011) — 10 citations