What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- World Wide Web
His primary scientific interests are in Information retrieval, World Wide Web, Data mining, Artificial intelligence and Pattern recognition.
His Information retrieval study incorporates themes from Metadata and Cluster analysis.
As a member of one scientific family, he mostly works in the field of World Wide Web, focusing on Content and, on occasion, Similarity.
His Data mining research is multidisciplinary, incorporating perspectives in Link farm, HITS algorithm, Classifier, Web page and Link analysis.
His work on Natural language processing expands to the thematically related Artificial intelligence.
In general Pattern recognition, his work in Dimensionality reduction is often linked to Graph theory linking many areas of study.
His most cited work include:
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction (2213 citations)
- Graph embedding: a general framework for dimensionality reduction (274 citations)
- Query-based snippet clustering for search result grouping (209 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Information retrieval, Artificial intelligence, Data mining, Pattern recognition and Web page.
His study connects World Wide Web and Information retrieval.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Natural language processing.
His Data mining research incorporates elements of Algorithm, Principal component analysis, Similarity and Link analysis.
In his study, which falls under the umbrella issue of Similarity, Document retrieval is strongly linked to Content.
The concepts of his Pattern recognition study are interwoven with issues in Subspace topology and Clustering high-dimensional data.
He most often published in these fields:
- Information retrieval (39.26%)
- Artificial intelligence (34.81%)
- Data mining (31.11%)
What were the highlights of his more recent work (between 2006-2009)?
- Information retrieval (39.26%)
- Spatial query (5.93%)
- Artificial intelligence (34.81%)
In recent papers he was focusing on the following fields of study:
Benyu Zhang spends much of his time researching Information retrieval, Spatial query, Artificial intelligence, Data mining and Query optimization.
His studies deal with areas such as Content and Web site as well as Information retrieval.
His Spatial query study combines topics from a wide range of disciplines, such as Visualization and Granger causality.
Benyu Zhang has researched Artificial intelligence in several fields, including Natural language processing and Pattern recognition.
His work deals with themes such as Attribute–value pair, Web mining and Web browser, which intersect with Data mining.
Benyu Zhang interconnects Query language, Query expansion and Web search query, Web query classification, Sargable in the investigation of issues within Query optimization.
Between 2006 and 2009, his most popular works were:
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction (2213 citations)
- Privacy-enhancing personalized web search (166 citations)
- Document Transformation for Multi-label Feature Selection in Text Categorization (88 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Benyu Zhang mainly investigates Artificial intelligence, World Wide Web, Natural language processing, Phrase and Latent semantic analysis.
His Artificial intelligence study integrates concerns from other disciplines, such as Connectivity and Directed graph.
His work on Theme and The Internet as part of general World Wide Web research is frequently linked to Popularity and Filter, bridging the gap between disciplines.
His Text mining study, which is part of a larger body of work in Natural language processing, is frequently linked to Document transformation, bridging the gap between disciplines.
His Phrase research integrates issues from Speech recognition and Phrase search.
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