230 citations · 1.7k across the 71 of their papers we have counts for
6 papers · 1 filter
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data
Guansong Pang, Anton van den Hengel, Chunhua Shen +1
We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important…
Deep Anomaly Detection with Deviation Networks
Guansong Pang, Chunhua Shen, Anton van den Hengel
Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing de…
Structured Learning of Binary Codes with Column Generation
Guosheng Lin, Fayao Liu, Chunhua Shen +2
Hashing methods aim to learn a set of hash functions which map the original features to compact binary codes with similarity preserving in the Hamming space. Hashing has proven a v…
An Efficient Dual Approach to Distance Metric Learning
Chunhua Shen, Junae Kim, Fayao Liu +2
Distance metric learning is of fundamental interest in machine learning because the distance metric employed can significantly affect the performance of many learning methods. Quad…
RandomBoost: Simplified Multi-class Boosting through Randomization
Sakrapee Paisitkriangkrai, Chunhua Shen, Qinfeng Shi +1
We propose a novel boosting approach to multi-class classification problems, in which multiple classes are distinguished by a set of random projection matrices in essence. The appr…
A Direct Approach to Multi-class Boosting and Extensions
Chunhua Shen, Sakrapee Paisitkriangkrai, Anton van den Hengel
Boosting methods combine a set of moderately accurate weaklearners to form a highly accurate predictor. Despite the practical importance of multi-class boosting, it has received fa…