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.
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 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.
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.
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.
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Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
european conference on machine learning (1998)
Making large-scale SVM learning practical
Research Papers in Economics (1998)
Optimizing search engines using clickthrough data
knowledge discovery and data mining (2002)
Transductive Inference for Text Classification using Support Vector Machines
international conference on machine learning (1999)
Large Margin Methods for Structured and Interdependent Output Variables
Ioannis Tsochantaridis;Thorsten Joachims;Thomas Hofmann;Yasemin Altun.
Journal of Machine Learning Research (2005)
Training linear SVMs in linear time
knowledge discovery and data mining (2006)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
international conference on machine learning (1997)
Making large-scale support vector machine learning practical
Advances in kernel methods (1999)
Support vector machine learning for interdependent and structured output spaces
Ioannis Tsochantaridis;Thomas Hofmann;Thorsten Joachims;Yasemin Altun.
international conference on machine learning (2004)
Profile was last updated on December 6th, 2021.
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