What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- The Internet
Roberto Battiti mainly investigates Artificial intelligence, Mathematical optimization, Machine learning, Algorithm and Local search.
His Artificial neural network, Backpropagation, Optical character recognition and Test set study in the realm of Artificial intelligence interacts with subjects such as Temporal discretization.
In general Artificial neural network study, his work on Supervised learning often relates to the realm of Position, thereby connecting several areas of interest.
In most of his Mathematical optimization studies, his work intersects topics such as Gradient descent.
His Algorithm research incorporates themes from Statistical learning theory, Adaptive system, Data set and Graph partition.
His Local search research incorporates elements of Beam search and Metaheuristic.
His most cited work include:
- Using mutual information for selecting features in supervised neural net learning (1901 citations)
- First- and second-order methods for learning: between steepest descent and Newton's method (992 citations)
- The Reactive Tabu Search (729 citations)
What are the main themes of his work throughout his whole career to date?
Mathematical optimization, Local search, Artificial intelligence, Algorithm and Computer network are his primary areas of study.
His research on Local search also deals with topics like
- Reinforcement learning which connect with Heuristics,
- Metaheuristic and related Ant colony optimization algorithms.
Roberto Battiti combines subjects such as Machine learning, Computer vision and Pattern recognition with his study of Artificial intelligence.
In the subject of general Algorithm, his work in Computation is often linked to Central processing unit and Clique problem, thereby combining diverse domains of study.
His studies deal with areas such as IEEE 802.11, Throughput, Distributed coordination function and Distributed computing as well as Computer network.
Roberto Battiti has included themes like Combinatorial optimization and Benchmark in his Tabu search study.
He most often published in these fields:
- Mathematical optimization (31.54%)
- Local search (23.49%)
- Artificial intelligence (21.48%)
What were the highlights of his more recent work (between 2011-2021)?
- Mathematical optimization (31.54%)
- Artificial intelligence (21.48%)
- Local search (23.49%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Mathematical optimization, Artificial intelligence, Local search, Optimization problem and Data mining.
His research integrates issues of Reservation and Benchmark in his study of Mathematical optimization.
The Artificial intelligence study combines topics in areas such as Machine learning, Conflicting objectives and Pattern recognition.
His Local search research includes themes of Artificial neural network and Stochastic neural network.
His Optimization problem research focuses on subjects like Multi-objective optimization, which are linked to Active learning and Pareto principle.
His Data mining study combines topics from a wide range of disciplines, such as Biclustering and Cluster analysis.
Between 2011 and 2021, his most popular works were:
- MOEA/D-ACO: A Multiobjective Evolutionary Algorithm Using Decomposition and AntColony (178 citations)
- Hybridization of decomposition and local search for multiobjective optimization. (76 citations)
- Active Learning of Pareto Fronts (27 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- The Internet
His main research concerns Mathematical optimization, Multi-objective optimization, Optimization problem, Heuristic and Metaheuristic.
His research on Mathematical optimization frequently connects to adjacent areas such as Active learning.
The concepts of his Optimization problem study are interwoven with issues in Canopy clustering algorithm, Cluster analysis, Fuzzy clustering, Local search and Knapsack problem.
His Heuristic research includes elements of Correlation clustering and Constrained clustering.
He studied Metaheuristic and Theoretical computer science that intersect with Algorithm.
His Algorithm study integrates concerns from other disciplines, such as Separable space, Entropy, Entropy, Feature extraction and Feature selection.
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