activity
20182021
most citedA Novel Random Forest Dissimilarity Measure for Multi-View Learning

3 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

cs.IR20212 cited

Destination similarity based on implicit user interest

Hongliu Cao, Eoin Thomas

With the digitization of travel industry, it is more and more important to understand users from their online behaviors. However, online travel industry data are more challenging t…

cs.LG20203 cited

A Novel Random Forest Dissimilarity Measure for Multi-View Learning

Hongliu Cao, Simon Bernard, Robert Sabourin +1

Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between t…

cs.CV2018

Dynamic voting in multi-view learning for radiomics applications

Hongliu Cao, Simon Bernard, Laurent Heutte +1

Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radi…

cs.CV2018

Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images

Hongliu Cao, Simon Bernard, Laurent Heutte +1

Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology I…

cs.CV2018

Dissimilarity-based representation for radiomics applications

Hongliu Cao, Simon Bernard, Laurent Heutte +1

Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognosti…