11 citations · 23 across the 6 of their papers we have counts for
9 papers
Learning to Detect Fortified Areas
Allan Grønlund, Jonas Tranberg
High resolution data models like grid terrain models made from LiDAR data are a prerequisite for modern day Geographic Information Systems applications. Besides providing the found…
Margins are Insufficient for Explaining Gradient Boosting
Allan Grønlund, Lior Kamma, Kasper Green Larsen
Boosting is one of the most successful ideas in machine learning, achieving great practical performance with little fine-tuning. The success of boosted classifiers is most often at…
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Allan Grønlund, Lior Kamma, Kasper Green Larsen
Support Vector Machines (SVMs) are among the most fundamental tools for binary classification. In its simplest formulation, an SVM produces a hyperplane separating two classes of d…
Learning to Find Hydrological Corrections
Lars Arge, Allan Grønlund, Svend Christian Svendsen +1
High resolution Digital Elevation models, such as the (Big) grid terrain model of Denmark with more than 200 billion measurements, is a basic requirement for water flow modelling a…
Margin-Based Generalization Lower Bounds for Boosted Classifiers
Allan Grønlund, Lior Kamma, Kasper Green Larsen +2
Boosting is one of the most successful ideas in machine learning. The most well-accepted explanations for the low generalization error of boosting algorithms such as AdaBoost stem…
Algorithms Clearly Beat Gamers at Quantum Moves. A Verification
Allan Grønlund
The paper [Sørensen et al., Nature 532] considers how human players compare to algorithms for solving the Quantum Moves game BringHomeWater and design new algorithms based on the i…