What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Digit recognition, Pattern recognition, Algorithm and Intelligent word recognition.
Corinna Cortes regularly ties together related areas like Machine learning in her Artificial intelligence studies.
Corinna Cortes works mostly in the field of Digit recognition, limiting it down to topics relating to Speech recognition and, in certain cases, Classifier, as a part of the same area of interest.
The concepts of her Algorithm study are interwoven with issues in Kernel method, Kernel, Support vector machine, Stability and Linear combination.
Many of her research projects under Kernel are closely connected to Bias of an estimator with Bias of an estimator, tying the diverse disciplines of science together.
In her study, Mathematical optimization is strongly linked to Simple, which falls under the umbrella field of Stability.
Her most cited work include:
- Support-Vector Networks (27201 citations)
- Comparison of classifier methods: a case study in handwritten digit recognition (507 citations)
- Comparison of classifier methods: a case study in handwritten digit recognition (507 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Artificial intelligence, Algorithm, Machine learning, Theoretical computer science and Mathematical optimization.
Her study explores the link between Artificial intelligence and topics such as Pattern recognition that cross with problems in Digit recognition and Speech recognition.
Her Algorithm research is multidisciplinary, incorporating elements of Quantum finite automata, Support vector machine, Kernel and Kernel.
Corinna Cortes has researched Kernel in several fields, including Stability, Feature vector and Rademacher complexity.
Her Machine learning study combines topics from a wide range of disciplines, such as Data mining and Series.
Her study in Mathematical optimization is interdisciplinary in nature, drawing from both Function and Regression.
She most often published in these fields:
- Artificial intelligence (35.21%)
- Algorithm (28.87%)
- Machine learning (17.61%)
What were the highlights of her more recent work (between 2017-2020)?
- Algorithm (28.87%)
- Theoretical computer science (15.49%)
- Regret (5.63%)
In recent papers she was focusing on the following fields of study:
Corinna Cortes mainly investigates Algorithm, Theoretical computer science, Regret, Generalization error and Active learning.
Her research in Algorithm intersects with topics in Normalization, Generative grammar, Discriminative model and Adaptation.
Her Regret research is multidisciplinary, incorporating perspectives in Key, Group and Online algorithm.
The various areas that she examines in her Key study include Structured prediction, Path and Computation.
She has researched Online algorithm in several fields, including Series and Extension.
She performs multidisciplinary study in the fields of Active learning and Probability mass function via her papers.
Between 2017 and 2020, her most popular works were:
- Adaptation Based on Generalized Discrepancy (43 citations)
- AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles (10 citations)
- Relative Deviation Learning Bounds and Generalization with Unbounded Loss Functions (8 citations)
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