What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Information retrieval
Linjun Yang focuses on Information retrieval, Ranking, Relevance, Feature and Image.
His research on Information retrieval often connects related areas such as Modality.
The Ranking study combines topics in areas such as Ranking, Full text search and Feature set.
His Ranking study combines topics from a wide range of disciplines, such as Social media and Similarity.
His Relevance research integrates issues from Multimedia, The Internet and Service.
He has included themes like Video tracking, Histogram, User profile and Inverted index in his Feature study.
His most cited work include:
- Tag ranking (456 citations)
- Ensemble Manifold Regularization (190 citations)
- Learning to tag (180 citations)
What are the main themes of his work throughout his whole career to date?
Linjun Yang mainly focuses on Information retrieval, Artificial intelligence, Machine learning, Ranking and Pattern recognition.
His studies deal with areas such as Image retrieval and Visual Word as well as Information retrieval.
In general Artificial intelligence, his work in Feature extraction, Image and Feature is often linked to TRECVID linking many areas of study.
His biological study spans a wide range of topics, including Ranking, Full text search, Web page and Data mining.
Linjun Yang interconnects Contextual image classification, Embedding and Cluster analysis in the investigation of issues within Pattern recognition.
Linjun Yang has researched Relevance in several fields, including Online video and Multimedia.
He most often published in these fields:
- Information retrieval (53.93%)
- Artificial intelligence (49.44%)
- Machine learning (23.60%)
What were the highlights of his more recent work (between 2012-2020)?
- Information retrieval (53.93%)
- Artificial intelligence (49.44%)
- World Wide Web (6.74%)
In recent papers he was focusing on the following fields of study:
Linjun Yang spends much of his time researching Information retrieval, Artificial intelligence, World Wide Web, The Internet and Search engine.
His Image retrieval research extends to the thematically linked field of Information retrieval.
The various areas that he examines in his Artificial intelligence study include Machine learning, Computer vision and Pattern recognition.
His work in the fields of Search-oriented architecture overlaps with other areas such as Bridging.
His biological study deals with issues like Automatic summarization, which deal with fields such as Data mining.
Linjun Yang has researched Ranking in several fields, including Search analytics, Concept search and Search engine indexing.
Between 2012 and 2020, his most popular works were:
- CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise (139 citations)
- Clickage: towards bridging semantic and intent gaps via mining click logs of search engines (93 citations)
- GPS Estimation for Places of Interest From Social Users' Uploaded Photos (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Information retrieval
Upload, Feature, Data mining, World Wide Web and Information retrieval are his primary areas of study.
Linjun Yang combines subjects such as Social media and The Internet with his study of Upload.
Linjun Yang integrates Feature with Assisted GPS in his research.
The study incorporates disciplines such as Ranking, Object, Relevance and Visual Word in addition to Data mining.
His research in the fields of Semantic search and Search engine overlaps with other disciplines such as Bridging.
His Information retrieval study incorporates themes from Language model and Automatic image annotation, Image retrieval.
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