Dragoljub Pokrajac

Artificial Intelligence · 2007

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #10,028 of 35,345 by trust here, #18,186 by citations
• Signal Processing — 14 citations into it; #4,523 of 7,572 by trust here, #5,570 by citations
• Computer Networks and Communications — 13 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Statistics and Probability — 7 citations into it

Whom Dragoljub Pokrajac trusts overall

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

1. Aleksandar Lazarevic
2. Eleazar Eskin
3. Stefan Berchtold
4. Hans-Peter Kriegel
5. Vipin Kumar
6. Richard P. Lippmann
7. Andrew O. Arnold
8. Spiros E. Papadimitriou
9. Daniel A. Keim
10. Foster J. Provost

Who trusts Dragoljub Pokrajac overall

1. Charu C. Aggarwal
2. Tomás Pevný
3. Hongzhi Wang
4. Varun Chandola
5. Jonathan J. Davis
6. Sebastian Schmidl
7. Stephen Ranshous
8. Mohamed Jaward Bah
9. Arindam Banerjee
10. Andrew J. Clark

Papers from Dragoljub Pokrajac's perspective

Ordered by how strongly the researchers Dragoljub Pokrajac trusts endorse each paper. Same corpus, a different order for every researcher.

1. LOF: identifying density-based local outliers
2. Robust Classification for Imprecise Environments
3. Outlier Detection Using Replicator Neural Networks (International Conference on Data Warehousing and Knowledge Discovery)
4. Feature bagging for outlier detection (Knowledge Discovery and Data Mining)
5. A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection
6. SMOTEBoost: Improving Prediction of the Minority Class in Boosting
7. LOCI: fast outlier detection using the local correlation integral (Proceedings / International Conference on Data Engineering)
8. A nonparametric estimate of a multivariate density function
9. Variable Kernel Estimates of Multivariate Densities
10. A unifying framework for detecting outliers and change points from non-stationary time series data (Knowledge Discovery and Data Mining)

Their papers

• Outlier Detection with Kernel Density Functions (2007) — 325 citations
• Incremental Local Outlier Detection for Data Streams (2007) — 451 citations