5 papers
Classification Tree-based Active Learning: A Wrapper Approach
Ashna Jose, Emilie Devijver, Massih-Reza Amini +2
Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes cruci…
Pool-Based Active Learning with Proper Topological Regions
Lies Hadjadj, Emilie Devijver, Remi Molinier +1
Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications. Pool-based active learning m…
Deep Learning with Partially Labeled Data for Radio Map Reconstruction
Alkesandra Malkova, Massih-Reza Amini, Benoit Denis +1
In this paper, we address the problem of Received Signal Strength map reconstruction based on location-dependent radio measurements and utilizing side knowledge about the local reg…
Recommender systems: when memory matters
Aleksandra Burashnikova, Marianne Clausel, Massih-Reza Amini +2
In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback. We propose an online algorithm, where m…
Algorithmic Robustness for Learning via -Good Similarity Functions
Maria-Irina Nicolae, Marc Sebban, Amaury Habrard +2
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking. However, it is worth noting that there is a severe lack of theoret…