D-Index & Metrics Best Publications

D-Index & Metrics 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.

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 76 Citations 80,005 215 World Ranking 764 National Ranking 457

Research.com Recognitions

Awards & Achievements

2015 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the theory and practice of machine learning and information retrieval

2014 - ACM Fellow For contributions to the theory and practice of machine learning and information retrieval.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Support vector machine

His primary areas of study are Artificial intelligence, Machine learning, Support vector machine, Structured support vector machine and Relevance vector machine. His Artificial intelligence research includes themes of Task and Flexibility. The Machine learning study which covers Classifier that intersects with Text processing and Probabilistic analysis of algorithms.

His Support vector machine research integrates issues from Time complexity, Statistical learning and Discriminative model. In his study, Online machine learning is strongly linked to Computational learning theory, which falls under the umbrella field of Structured support vector machine. His Text categorization research is multidisciplinary, relying on both Variety, Document processing and Natural language processing.

His most cited work include:

  • Text Categorization with Suport Vector Machines: Learning with Many Relevant Features (6944 citations)
  • Making large scale SVM learning practical (4028 citations)
  • Optimizing search engines using clickthrough data (3726 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Machine learning, Support vector machine, Ranking and Information retrieval. When carried out as part of a general Artificial intelligence research project, his work on Ranking is frequently linked to work in Estimator, therefore connecting diverse disciplines of study. His Machine learning study combines topics from a wide range of disciplines, such as Training set and Data mining.

His work on Structured support vector machine is typically connected to Set as part of general Support vector machine study, connecting several disciplines of science. His Ranking research incorporates elements of Relevance, Search engine and Operations research. His studies in Information retrieval integrate themes in fields like World Wide Web and Eye tracking.

He most often published in these fields:

  • Artificial intelligence (56.72%)
  • Machine learning (45.80%)
  • Support vector machine (23.95%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (56.72%)
  • Machine learning (45.80%)
  • Ranking (21.01%)

In recent papers he was focusing on the following fields of study:

His main research concerns Artificial intelligence, Machine learning, Ranking, Estimator and Recommender system. Thorsten Joachims performs multidisciplinary study in Artificial intelligence and Property in his work. His study in Machine learning is interdisciplinary in nature, drawing from both Counterfactual thinking and Representation.

His study in the fields of Learning to rank under the domain of Ranking overlaps with other disciplines such as Rank. His study explores the link between Learning to rank and topics such as Inference that cross with problems in Ranking SVM, Support vector machine and Training set. His Recommender system research incorporates themes from Robustness, Causal inference and Human–computer interaction.

Between 2015 and 2021, his most popular works were:

  • Unbiased Learning-to-Rank with Biased Feedback (217 citations)
  • Fairness of Exposure in Rankings (164 citations)
  • Recommendations as Treatments: Debiasing Learning and Evaluation (89 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Thorsten Joachims mainly investigates Artificial intelligence, Machine learning, Ranking, Recommender system and Estimator. The concepts of his Artificial intelligence study are interwoven with issues in Stability and Minimax. His Machine learning study integrates concerns from other disciplines, such as Counterfactual thinking and Causal inference.

His Ranking research includes elements of Operations research, Feature learning and Pairwise comparison. His work investigates the relationship between Recommender system and topics such as Human–computer interaction that intersect with problems in Multimedia. He works mostly in the field of Learning to rank, limiting it down to topics relating to Inference and, in certain cases, Relaxation, Mathematical optimization, Support vector machine and Training set, as a part of the same area of interest.

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

Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

Thorsten Joachims.
european conference on machine learning (1998)

12159 Citations

Making large-scale SVM learning practical

Thorsten Joachims.
Research Papers in Economics (1998)

8104 Citations

Making large scale SVM learning practical

Thorsten Joachims.
Technical reports (1999)

7508 Citations

Optimizing search engines using clickthrough data

Thorsten Joachims.
knowledge discovery and data mining (2002)

5260 Citations

Transductive Inference for Text Classification using Support Vector Machines

Thorsten Joachims.
international conference on machine learning (1999)

3905 Citations

Large Margin Methods for Structured and Interdependent Output Variables

Ioannis Tsochantaridis;Thorsten Joachims;Thomas Hofmann;Yasemin Altun.
Journal of Machine Learning Research (2005)

2640 Citations

Training linear SVMs in linear time

Thorsten Joachims.
knowledge discovery and data mining (2006)

2446 Citations

Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms

Thorsten Joachims.
(2002)

2236 Citations

A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

Thorsten Joachims.
international conference on machine learning (1997)

2203 Citations

Making large-scale support vector machine learning practical

Thorsten Joachims.
Advances in kernel methods (1999)

2057 Citations

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