Anomaly Detection in Vertically Partitioned Data by Distributed Core Vector Machines
By Marco Stolpe, Kanishka Bhaduri, K. Das, Katharina Morik. 2013
21 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Wojciech Samek
- Grgoire Montavon
- K. Müller
- Klaus‐Robert Müller
- Praneeth Vepakomma
- Abhishek Singh
- Ramesh Raskar
- Vivek Sharma
In Computer Networks and Communications
- Thomas G. Dietterich
- Grgoire Montavon
- Ramesh Raskar
- Wojciech Samek
- Marius Kloft
- Praneeth Vepakomma
- Robert A. Vandermeulen
- Lukas Ruff
In Signal Processing
- Thomas G. Dietterich
- Grgoire Montavon
- Ramesh Raskar
- Wojciech Samek
- Abhishek Singh
- Marius Kloft
- Praneeth Vepakomma
- Robert A. Vandermeulen
Citations in the corpus
- SplitNN-driven Vertical Partitioning (2020, background): “Similarly, the work in (Stolpe et al. , 2013) shows a simpler approach of using core vector machines for anomaly detection using vertically partitioned data.”
- A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “Many variants of kernel one-class classification have been proposed and studied over the years, such as hierarchical formulations for nested density level set estimation [314], [315], multisphere SVDD [316], multiple kernel learning for OC-SVM [317], [318], OC-SVM for group AD [197], boosting via L1”