most citedMulti-Sensor Multi-object Tracking with the Generalized Labeled Multi-Bernoulli Filter

4 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20172 cited

Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric

Quang N. Tran, Ba-Ngu Vo, Dinh Phung +2

Multiple instance data are sets or multi-sets of unordered elements. Using metrics or distances for sets, we propose an approach to several multiple instance learning tasks, such a…

stat.CO20174 cited

Multi-Sensor Multi-object Tracking with the Generalized Labeled Multi-Bernoulli Filter

Ba Ngu Vo, Ba Tuong Vo

This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update to…

cs.LG2017

Clustering For Point Pattern Data

Quang N. Tran, Ba-Ngu Vo, Dinh Phung +1

Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across seve…

cs.LG2017

Model-based Classification and Novelty Detection For Point Pattern Data

Ba-Ngu Vo, Quang N. Tran, Dinh Phung +1

Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection…

stat.CO20153 cited

A Generalized Labeled Multi-Bernoulli Filter Implementation using Gibbs Sampling

Hung Gia Hoang, Ba-Tuong Vo, Ba-Ngu Vo

This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step. In contrast to t…