D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 42 Citations 63,446 91 World Ranking 4044 National Ranking 2053

Research.com Recognitions

Awards & Achievements

2008 - ACM Paris Kanellakis Theory and Practice Award For the development of Support Vector Machines, a highly effective algorithm for classification and related machine learning problems.

Overview

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)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Support-Vector Networks

Corinna Cortes;Vladimir Vapnik.
Machine Learning (1995)

48415 Citations

Comparison of classifier methods: a case study in handwritten digit recognition

L. Bottou;C. Cortes;C. Cortes;J.S. Denker;J.S. Denker;H. Drucker;H. Drucker.
international conference on pattern recognition (1994)

882 Citations

Learning algorithms for classification: A comparison on handwritten digit recognition

Yann Lecun;L.D. Jackel;Leon Bottou;Leon Bottou;Corinna Cortes;Corinna Cortes.
(1995)

831 Citations

Comparison of learning algorithms for handwritten digit recognition

Yann Lecun;L.D. Jackel;Leon Bottou;Leon Bottou;A. Brunot.
(1995)

775 Citations

AUC Optimization vs. Error Rate Minimization

Corinna Cortes;Mehryar Mohri.
neural information processing systems (2003)

667 Citations

Boosting and other ensemble methods

Harris Drucker;Corinna Cortes;L. D. Jackel;Yann LeCun.
Neural Computation (1994)

425 Citations

Algorithms for learning kernels based on centered alignment

Corinna Cortes;Mehryar Mohri;Afshin Rostamizadeh.
Journal of Machine Learning Research (2012)

362 Citations

Boosting Decision Trees

Harris Drucker;Corinna Cortes.
neural information processing systems (1995)

345 Citations

Learning Non-Linear Combinations of Kernels

Corinna Cortes;Mehryar Mohri;Afshin Rostamizadeh.
neural information processing systems (2009)

338 Citations

Sample Selection Bias Correction Theory

Corinna Cortes;Mehryar Mohri;Michael Riley;Afshin Rostamizadeh.
algorithmic learning theory (2008)

293 Citations

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