15 citations · 32 across the 7 of their papers we have counts for
7 papers
Random Forest Kernel for High-Dimension Low Sample Size Classification
Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal +1
High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learnin…
Domain Translation via Latent Space Mapping
Tsiry Mayet, Simon Bernard, Clement Chatelain +1
In this paper, we investigate the problem of multi-domain translation: given an element of domain , we would like to generate a corresponding sample in another domain $B…
Random Forest for Dissimilarity-based Multi-view Learning
Simon Bernard, Hongliu Cao, Robert Sabourin +1
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies ar…
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…
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…
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…