Scott Purdy

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 13 citations into it
• Computer Networks and Communications — 8 citations into it

Whom Scott Purdy trusts overall

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

1. Ryan P. Adams
2. Jeff Hawkins
3. Alexander Lavin
4. Subutai Ahmad

Papers from Scott Purdy's perspective

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

1. Anomaly detection: A survey
2. HOT SAX: efficiently finding the most unusual time series subsequence (Industrial Conference on Data Mining)
3. Diagnosing network-wide traffic anomalies
4. Automatic Time Series Forecasting: The forecast Package for R
5. Knowledge discovery from data streams
6. Toward Supervised Anomaly Detection (Journal of Artificial Intelligence Research)
7. Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark (International Conference on Machine Learning and Applications)
8. Generic and Scalable Framework for Automated Time-series Anomaly Detection (Knowledge Discovery and Data Mining)
9. A review of novelty detection
10. Bayesian Online Changepoint Detection

Their papers

• Unsupervised real-time anomaly detection for streaming data (2017) — 953 citations