Institut für Informatik und Computational Science
Refine
Has Fulltext
- no (29)
Document Type
- Article (27)
- Monograph/Edited Volume (2)
Language
- English (29) (remove)
Is part of the Bibliography
- yes (29)
Keywords
- EEG (1)
- brain-computer interface (1)
- common spatial patterns (1)
- drug discovery (1)
- event-related desynchronization (1)
- incremental SVM (1)
- intrusion detection (1)
- online learning (1)
- single-trial-analysis (1)
Institute
We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach.
An asymptotic analysis and improvement of AdaBoost in the binary classification case (in Japanese)
(2000)
SVM and boosting : one class
(2000)
Unmixing hyperspectral data
(2000)
Robust ensemble learning
(2000)