What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- World Wide Web
Artificial intelligence, Machine learning, Semi-supervised learning, Data mining and Domain are his primary areas of study.
His work in the fields of Artificial intelligence, such as Transfer of learning and Cluster analysis, intersects with other areas such as Multi-task learning.
His work on Labeled data as part of general Machine learning research is often related to Test data, thus linking different fields of science.
His work deals with themes such as Iterative method, Boosting, Leverage and Generalization error, which intersect with Labeled data.
His Data mining study integrates concerns from other disciplines, such as Biclustering and Collaborative filtering, MovieLens.
His Web page research is multidisciplinary, relying on both Search analytics and Web search query.
His most cited work include:
- Boosting for transfer learning (1114 citations)
- Scalable collaborative filtering using cluster-based smoothing (558 citations)
- Optimizing web search using social annotations (526 citations)
What are the main themes of his work throughout his whole career to date?
Gui-Rong Xue spends much of his time researching Information retrieval, Artificial intelligence, Data mining, Web page and Machine learning.
The Information retrieval study combines topics in areas such as Data Web and Web modeling.
Many of his research projects under Artificial intelligence are closely connected to Multi-task learning with Multi-task learning, tying the diverse disciplines of science together.
His Data mining study combines topics from a wide range of disciplines, such as Search engine, Cluster analysis, Link analysis, PageRank and Algorithm.
Gui-Rong Xue has included themes like Similarity and The Internet in his Web page study.
The various areas that Gui-Rong Xue examines in his Machine learning study include Classifier and Training set.
He most often published in these fields:
- Information retrieval (41.86%)
- Artificial intelligence (40.70%)
- Data mining (38.37%)
What were the highlights of his more recent work (between 2009-2012)?
- Artificial intelligence (40.70%)
- Machine learning (31.40%)
- Information retrieval (41.86%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Information retrieval, World Wide Web and Online advertising.
He combines subjects such as Ranking and Data mining with his study of Artificial intelligence.
His Ranking study combines topics in areas such as Boosting and Search engine.
The Ranking SVM and Learning to rank research Gui-Rong Xue does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Multi-task learning, therefore creating a link between diverse domains of science.
His work on Click path, Organic search and Precision and recall as part of general Information retrieval research is frequently linked to User experience design and Product, bridging the gap between disciplines.
In his work, Feature vector is strongly intertwined with Language model, which is a subfield of Web page.
Between 2009 and 2012, his most popular works were:
- Heterogeneous transfer learning for image classification (195 citations)
- Knowledge transfer for cross domain learning to rank (38 citations)
- Advertising Keywords Recommendation for Short-Text Web Pages Using Wikipedia (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- World Wide Web
His scientific interests lie mostly in Machine learning, Artificial intelligence, Contextual advertising, PageRank and Information retrieval.
His study in the field of Ranking SVM, Support vector machine and Ranking also crosses realms of Rank.
His work on Learning to rank and Semi-supervised learning is typically connected to Domain, Social web and Inductive transfer as part of general Artificial intelligence study, connecting several disciplines of science.
The study incorporates disciplines such as Web analytics, Web page and Keyword advertising in addition to Contextual advertising.
PageRank and Online advertising are frequently intertwined in his study.
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