What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Barbara Hammer mostly deals with Artificial intelligence, Learning vector quantization, Machine learning, Vector quantization and Artificial neural network.
Her research in Artificial intelligence intersects with topics in Data mining and Pattern recognition.
Her studies deal with areas such as Constrained clustering, Relevance and Kernel as well as Pattern recognition.
Her studies in Learning vector quantization integrate themes in fields like Semi-supervised learning and Competitive learning.
Her work deals with themes such as Stochastic gradient descent, Hebbian theory and Euclidean distance, which intersect with Vector quantization.
The various areas that Barbara Hammer examines in her Artificial neural network study include Algorithm, Deep learning and Metric.
Her most cited work include:
- Generalized relevance learning vector quantization (369 citations)
- Adaptive relevance matrices in learning vector quantization (254 citations)
- Neural maps in remote sensing image analysis (152 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Artificial intelligence, Machine learning, Pattern recognition, Learning vector quantization and Artificial neural network.
The concepts of her Artificial intelligence study are interwoven with issues in Data mining and Metric.
Barbara Hammer combines subjects such as Matrix, Pairwise comparison and Curse of dimensionality with her study of Pattern recognition.
Her biological study spans a wide range of topics, including Gradient descent, Function, Fuzzy logic and Euclidean distance.
Her work in Artificial neural network addresses issues such as Deep learning, which are connected to fields such as Types of artificial neural networks.
Her Dimensionality reduction research is multidisciplinary, incorporating perspectives in Visualization and Data visualization.
She most often published in these fields:
- Artificial intelligence (69.48%)
- Machine learning (43.30%)
- Pattern recognition (27.84%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (69.48%)
- Machine learning (43.30%)
- Artificial neural network (15.46%)
In recent papers she was focusing on the following fields of study:
Barbara Hammer spends much of her time researching Artificial intelligence, Machine learning, Artificial neural network, Concept drift and Pattern recognition.
Her work in Discriminative model, Feature selection, Feature vector, Kernel and Feature relevance are all subfields of Artificial intelligence research.
Her study in Machine learning is interdisciplinary in nature, drawing from both Class and Computation.
The Artificial neural network study combines topics in areas such as Adversarial system, Computer engineering and Benchmark.
Her Concept drift research incorporates elements of Supervised learning, Random forest, Memory architecture and Sigmoid function.
Her Learning vector quantization research includes themes of Differential privacy and Metric.
Between 2016 and 2021, her most popular works were:
- Incremental on-line learning: A review and comparison of state of the art algorithms (130 citations)
- flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry (23 citations)
- Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM) (18 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Her main research concerns Artificial intelligence, Machine learning, Training set, Learning vector quantization and Robustness.
The study incorporates disciplines such as Data stream mining, State and Parameterized complexity in addition to Artificial intelligence.
Barbara Hammer focuses mostly in the field of Machine learning, narrowing it down to topics relating to Computation and, in certain cases, Sigmoid function and Mathematical economics.
Her Training set study also includes
- Data modeling together with Statistical classification, Anomaly detection and Cluster analysis,
- Series, Predictive modelling, Structure and Transformation most often made with reference to Transfer of learning.
Her work investigates the relationship between Statistical classification and topics such as Data mining that intersect with problems in Algorithm.
Her work in Learning vector quantization addresses subjects such as Class, which are connected to disciplines such as Scale, Process and Metric.
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