Search Results for author:
Found 8 papers, 1 papers with code
Compression Implies Generalization
The framework is simple and powerful enough to extend the generalization bounds by Arora et al. to also hold for the original network.
Learning to Detect Fortified Areas
High resolution data models like grid terrain models made from LiDAR data are a prerequisite for modern day Geographic Information Systems applications.
Margins are Insufficient for Explaining Gradient Boosting
We then explain the short comings of the $k$'th margin bound and prove a stronger and more refined margin-based generalization bound for boosted classifiers that indeed succeeds in explaining the performance of modern gradient boosters.
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Support Vector Machines (SVMs) are among the most fundamental tools for binary classification.
Margin-Based Generalization Lower Bounds for Boosted Classifiers
To date, the strongest known generalization (upper bound) is the $k$th margin bound of Gao and Zhou (2013).
Learning to Find Hydrological Corrections
However, a large number of modifications often need to be made to even very accurate terrain models, such as the Danish model, before they can be used in realistic flow modeling.
Optimal Minimal Margin Maximization with Boosting
A common goal in a long line of research, is to maximize the smallest margin using as few base hypotheses as possible, culminating with the AdaBoostV algorithm by (R{\"a}tsch and Warmuth [JMLR'04]).
Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D
We present all the existing work that had been overlooked and compare the various solutions theoretically.
