What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Estimation of distribution algorithm, Bayesian network, Machine learning and Pattern recognition.
Pedro Larrañaga has included themes like Data mining and Bijection in his Artificial intelligence study.
His work deals with themes such as Evolutionary computation and Probabilistic logic, Probabilistic analysis of algorithms, which intersect with Estimation of distribution algorithm.
The study incorporates disciplines such as Heuristics and Bayes' theorem in addition to Machine learning.
His studies in Selection integrate themes in fields like Feature, Feature selection, Crossover and Gene expression profiling.
Pedro Larrañaga has researched Pattern recognition in several fields, including Minimum redundancy feature selection, Support vector machine, Taxonomy, Variety and Data science.
His most cited work include:
- A review of feature selection techniques in bioinformatics (3479 citations)
- Estimation of Distribution Algorithms (955 citations)
- An empirical comparison of four initialization methods for the K-Means algorithm (648 citations)
What are the main themes of his work throughout his whole career to date?
Pedro Larrañaga mainly investigates Artificial intelligence, Machine learning, Bayesian network, Estimation of distribution algorithm and Pattern recognition.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Data mining.
His work carried out in the field of Machine learning brings together such families of science as Class and Bayesian probability, Bayes' theorem.
His study in Bayesian network is interdisciplinary in nature, drawing from both Wake-sleep algorithm, Theoretical computer science, Class variable, Genetic algorithm and Intelligent control.
His Estimation of distribution algorithm research incorporates themes from Evolutionary computation and Probabilistic logic.
His Feature research extends to Selection, which is thematically connected.
He most often published in these fields:
- Artificial intelligence (52.17%)
- Machine learning (35.65%)
- Bayesian network (31.30%)
What were the highlights of his more recent work (between 2018-2021)?
- Bayesian network (31.30%)
- Artificial intelligence (52.17%)
- Machine learning (35.65%)
In recent papers he was focusing on the following fields of study:
His main research concerns Bayesian network, Artificial intelligence, Machine learning, Neuroscience and Process.
His Bayesian network research includes themes of Graphical model and Interpretability.
His Artificial intelligence study combines topics in areas such as Search algorithm, Adaptation and Pattern recognition.
His biological study spans a wide range of topics, including Visualization, Probabilistic logic, State and Gene regulatory network.
His study on Temporal cortex and Electrophysiology is often connected to Cell type, Nomenclature and Community based as part of broader study in Neuroscience.
His work is dedicated to discovering how Component, Autoregressive model are connected with Algorithm and Training set and other disciplines.
Between 2018 and 2021, his most popular works were:
- A community-based transcriptomics classification and nomenclature of neocortical cell types (29 citations)
- Learning tractable Bayesian networks in the space of elimination orders (8 citations)
- Classification of GABAergic interneurons by leading neuroscientists (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Pedro Larrañaga spends much of his time researching Bayesian network, Artificial intelligence, Machine learning, Neuroscience and Transcriptome.
The various areas that Pedro Larrañaga examines in his Bayesian network study include Computational complexity theory, Theoretical computer science, Inference and Time complexity.
His Artificial intelligence study frequently draws connections to other fields, such as Adaptation.
His research in Machine learning intersects with topics in Visualization, State, Gene regulatory network and Search algorithm.
His work in the fields of Neocortex overlaps with other areas such as Interneuron, Single-cell analysis and Data aggregator.
As a part of the same scientific family, Pedro Larrañaga mostly works in the field of Neocortex, focusing on Probabilistic logic and, on occasion, Feature, Artificial neural network, Computational neuroscience, Support vector machine and Graphical model.
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