H
Holger H. Hoos
Researcher at Leiden University
Publications - 318
Citations - 29853
Holger H. Hoos is an academic researcher from Leiden University. The author has contributed to research in topics: Local search (optimization) & Solver. The author has an hindex of 74, co-authored 298 publications receiving 25784 citations. Previous affiliations of Holger H. Hoos include Walter Reed National Military Medical Center & Darmstadt University of Applied Sciences.
Papers
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Journal ArticleDOI
MAX-MIN Ant system
Thomas Stützle,Holger H. Hoos +1 more
TL;DR: Computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that MM AS is currently among the best performing algorithms for these problems.
Book ChapterDOI
Sequential model-based optimization for general algorithm configuration
TL;DR: This paper extends the explicit regression models paradigm for the first time to general algorithm configuration problems, allowing many categorical parameters and optimization for sets of instances, and yields state-of-the-art performance.
Book
Stochastic Local Search: Foundations & Applications
Holger H. Hoos,Thomas Sttzle +1 more
TL;DR: This prologue explains the background to SLS, and some examples of applications can be found in SAT and Constraint Satisfaction, as well as some of the algorithms used to solve these problems.
Journal ArticleDOI
A survey on semi-supervised learning
TL;DR: This survey aims to provide researchers and practitioners new to the field as well as more advanced readers with a solid understanding of the main approaches and algorithms developed over the past two decades, with an emphasis on the most prominent and currently relevant work.
Proceedings ArticleDOI
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
TL;DR: In this article, the problem of simultaneously selecting a learning algorithm and setting its hyperparameters is addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization, which can help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications.