Your selections:
LeSiNN : Detecting anomalies by identifying least similar nearest neighbours
- Pang, Guansong, Ting, Kaiming, Albrecht, David
Isolation set-kernel and its application to multi-instance learning
- Xu, Bi-Cun, Ting, Kaiming, Zhou, Zhi-Hua
Defying the gravity of learning curve : A characteristic of nearest neighbour anomaly detectors
- Ting, Kaiming, Washio, Takashi, Wells, Jonathan, Aryal, Sunil
Data-dependent dissimilarity measure : An effective alternative to geometric distance measures
- Aryal, Sunil, Ting, Kaiming, Washio, Takashi, Haffari, Gholamreza
ZERO++ : Harnessing the power of zero appearances to detect anomalies in large-scale data sets
- Pang, Guansong, Ting, Kaiming, Albrecht, David, Jin, Huidong
LiNearN : A new approach to nearest neighbour density estimator
- Wells, Jonathan, Ting, Kaiming, Washio, Takashi
Half-space mass : a maximally robust and efficient data depth method
- Chen, Bo, Ting, Kaiming, Washio, Takashi, Haffari, Gholamreza
Beyond tf-idf and cosine distance in documents dissimilarity measure
- Aryal, Sunil, Ting, Kaiming, Haffari, Gholamreza, Washio, Takashi
Grouping points by shared subspaces for effective subspace clustering
- Zhu, Ye, Ting, Kaiming, Carman, Mark
Improving iForest with relative mass
- Aryal, Sunil, Ting, Kaiming, Wells, Jonathan, Washio, Takashi
Discover multiple novel labels in multi-instance multi-label learning
- Zhu, Yue, Ting, Kaiming, Zhou, Zhi-Hua
Isolation kernel and its effect on SVM
- Ting, Kaiming, Zhu, Yue, Zhou, Zhi-Hua
Isolation-based anomaly detection using nearest-neighbor ensembles
- Bandaragoda, Tharindu, Ting, Kaiming, Albrecht, David, Liu, Fei, Zhu, Ye, Wells, Jonathan
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