What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
César Hervás-Martínez mainly focuses on Artificial neural network, Artificial intelligence, Evolutionary computation, Machine learning and Data mining.
His Artificial neural network research integrates issues from Evolutionary algorithm, Genetic algorithm and Benchmark.
As part of his studies on Artificial intelligence, César Hervás-Martínez often connects relevant subjects like Pattern recognition.
In his study, which falls under the umbrella issue of Evolutionary computation, Multi-objective optimization is strongly linked to Cooperative coevolution.
In the subject of general Machine learning, his work in Structured support vector machine and Decision tree is often linked to Crop field, thereby combining diverse domains of study.
The study incorporates disciplines such as Ordinal data, Ordinal regression, Ordered logit and Cluster analysis in addition to Data mining.
His most cited work include:
- Ordinal Regression Methods: Survey and Experimental Study (196 citations)
- Cooperative coevolution of artificial neural network ensembles for pattern classification (191 citations)
- COVNET: a cooperative coevolutionary model for evolving artificial neural networks (184 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Artificial neural network, Machine learning, Pattern recognition and Evolutionary algorithm are his primary areas of study.
His Artificial intelligence study integrates concerns from other disciplines, such as Ordinal regression, Data mining and Ordinal data.
His work on Radial basis function as part of general Artificial neural network study is frequently connected to Basis function, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His biological study spans a wide range of topics, including Classifier and Liver transplantation.
César Hervás-Martínez combines subjects such as Extreme learning machine and Kernel with his study of Pattern recognition.
His Evolutionary algorithm research includes elements of Multi-objective optimization, Pareto principle and Local search.
He most often published in these fields:
- Artificial intelligence (63.36%)
- Artificial neural network (46.98%)
- Machine learning (36.64%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (63.36%)
- Artificial neural network (46.98%)
- Algorithm (15.09%)
In recent papers he was focusing on the following fields of study:
César Hervás-Martínez spends much of his time researching Artificial intelligence, Artificial neural network, Algorithm, Machine learning and Evolutionary algorithm.
His research is interdisciplinary, bridging the disciplines of Pattern recognition and Artificial intelligence.
His Artificial neural network research is mostly focused on the topic Sigmoid function.
César Hervás-Martínez has researched Algorithm in several fields, including Statistical hypothesis testing and Segmentation.
César Hervás-Martínez interconnects Matching, Liver transplantation and Survival analysis in the investigation of issues within Machine learning.
His work deals with themes such as Multi-objective optimization, Data mining and Topology, which intersect with Evolutionary algorithm.
Between 2017 and 2021, his most popular works were:
- A statistically-driven Coral Reef Optimization algorithm for optimal size reduction of time series (18 citations)
- Multi-task learning for the prediction of wind power ramp events with deep neural networks. (15 citations)
- Time series forecasting by recurrent product unit neural networks (14 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
His primary areas of study are Artificial intelligence, Machine learning, Artificial neural network, Algorithm and Segmentation.
His research integrates issues of Survival analysis and Donor selection in his study of Artificial intelligence.
His work carried out in the field of Machine learning brings together such families of science as Wind power and Renewable energy.
His Artificial neural network research incorporates themes from Evolutionary algorithm, Multi-objective optimization, Mathematical optimization and Ensemble forecasting.
The Evolutionary algorithm study combines topics in areas such as Binary classification, Classifier, Pareto principle and Multiclass classification.
His Segmentation study incorporates themes from Similarity, Local search and Cluster analysis.
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