What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
Holger H. Hoos spends much of his time researching Local search, Algorithm, Mathematical optimization, Machine learning and Artificial intelligence.
His work carried out in the field of Local search brings together such families of science as Heuristics, Heuristic, Vertex, Solver and Boolean satisfiability problem.
As a part of the same scientific family, Holger H. Hoos mostly works in the field of Algorithm, focusing on Categorical variable and, on occasion, Computational problem.
Travelling salesman problem and Optimization problem are the primary areas of interest in his Mathematical optimization study.
His Machine learning research integrates issues from Independence and Conditional dependence.
The concepts of his Artificial intelligence study are interwoven with issues in Mathematical economics, Ceteris paribus and Preference.
His most cited work include:
- MAX-MIN Ant system (2218 citations)
- Stochastic Local Search: Foundations & Applications (1165 citations)
- Sequential model-based optimization for general algorithm configuration (1111 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Algorithm, Local search, Mathematical optimization, Artificial intelligence and Solver.
His Algorithm research includes elements of Range, Function and Set.
He focuses mostly in the field of Local search, narrowing it down to matters related to Search algorithm and, in some cases, Stochastic programming.
His work in the fields of Travelling salesman problem, Iterated local search, Heuristic and Ant colony optimization algorithms overlaps with other areas such as Portfolio.
His research in Artificial intelligence focuses on subjects like Machine learning, which are connected to Data mining.
His Solver research focuses on Theoretical computer science and how it relates to Scheduling.
He most often published in these fields:
- Algorithm (33.45%)
- Local search (29.14%)
- Mathematical optimization (27.70%)
What were the highlights of his more recent work (between 2014-2020)?
- Algorithm (33.45%)
- Solver (16.19%)
- Mathematical optimization (27.70%)
In recent papers he was focusing on the following fields of study:
Holger H. Hoos focuses on Algorithm, Solver, Mathematical optimization, Set and Range.
His is doing research in State, Local search and Travelling salesman problem, both of which are found in Algorithm.
His Solver research incorporates themes from Time complexity, Algorithm design, Theoretical computer science and Parameterized complexity.
His research brings together the fields of Algorithm configuration and Mathematical optimization.
While the research belongs to areas of Set, he spends his time largely on the problem of Benchmark, intersecting his research to questions surrounding Hyperparameter and Population-based incremental learning.
His Range study combines topics from a wide range of disciplines, such as Quality, Variety, Machine learning and Hyperparameter optimization.
Between 2014 and 2020, his most popular works were:
- Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA (312 citations)
- A survey on semi-supervised learning (150 citations)
- A survey on semi-supervised learning (150 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Programming language
His scientific interests lie mostly in Mathematical optimization, Algorithm, Solver, Machine learning and Artificial intelligence.
The study incorporates disciplines such as Algorithm configuration and Set in addition to Mathematical optimization.
His Algorithm study incorporates themes from Lasso and Markov chain.
His Solver research incorporates elements of Theoretical computer science, Boolean satisfiability problem, Benchmark and Answer set programming.
His research in Machine learning intersects with topics in Recall, Data mining and Interface.
His study in the field of Codebook is also linked to topics like Running time.
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