What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Sara Silva mainly focuses on Genetic programming, Artificial intelligence, Machine learning, Overfitting and Limit.
Her Genetic programming research is multidisciplinary, incorporating elements of Theoretical computer science, Data mining, Semantics, Set and Fitness landscape.
Her work carried out in the field of Fitness landscape brings together such families of science as Training set, Genetic representation, Formal methods, Locality and Information retrieval.
Her Artificial intelligence study deals with Generalization intersecting with Domain.
Her work focuses on many connections between Machine learning and other disciplines, such as Test data, that overlap with her field of interest in A priori and a posteriori, State and Rating scale.
Her Overfitting study incorporates themes from Evolutionary dynamics, Symbolic regression, Operator and Control.
Her most cited work include:
- GPLAB A Genetic Programming Toolbox for MATLAB (250 citations)
- A survey of semantic methods in genetic programming (115 citations)
- Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories (110 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of investigation include Genetic programming, Artificial intelligence, Machine learning, Generalization and Algorithm.
Her Genetic programming research is multidisciplinary, relying on both Fitness landscape, Theoretical computer science, Overfitting and Operator.
Her work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Curse of dimensionality and Feature.
Her Machine learning research is multidisciplinary, incorporating perspectives in Test data, Land cover and Data mining.
The Generalization study combines topics in areas such as Artificial neural network, Genetic algorithm and Mutation.
Many of her research projects under Algorithm are closely connected to Limit and Tree-depth with Limit and Tree-depth, tying the diverse disciplines of science together.
She most often published in these fields:
- Genetic programming (90.36%)
- Artificial intelligence (64.46%)
- Machine learning (41.57%)
What were the highlights of her more recent work (between 2018-2021)?
- Genetic programming (90.36%)
- Artificial intelligence (64.46%)
- Machine learning (41.57%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Genetic programming, Artificial intelligence, Machine learning, Generalization and Feature vector.
Sara Silva is interested in Symbolic regression, which is a field of Genetic programming.
Her Artificial intelligence research includes themes of Adaptation and Pattern recognition.
The concepts of her Machine learning study are interwoven with issues in Land cover, Tree, Beauty and Operator.
Her Generalization study also includes
- Ensemble learning that connect with fields like Population structure and Random forest,
- Remote sensing which connect with Data visualization.
Her research integrates issues of Feature extraction, Feature and Curse of dimensionality in her study of Feature vector.
Between 2018 and 2021, her most popular works were:
- Multidimensional genetic programming for multiclass classification (24 citations)
- Multidimensional genetic programming for multiclass classification (24 citations)
- Multidimensional genetic programming for multiclass classification (24 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Sara Silva focuses on Genetic programming, Artificial intelligence, Feature vector, Machine learning and Curse of dimensionality.
Particularly relevant to Symbolic regression is her body of work in Genetic programming.
Her work on Principal component analysis as part of her general Artificial intelligence study is frequently connected to Volatility, thereby bridging the divide between different branches of science.
The study incorporates disciplines such as Algorithm, Local search, Feature and Mutual information in addition to Feature vector.
She has included themes like Generalization and Population structure in her Machine learning study.
Her Curse of dimensionality study combines topics from a wide range of disciplines, such as Multiclass classification and Feature extraction, Pattern recognition, Feature selection, Dimensionality reduction.
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