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, Artificial intelligence, Data mining, Cluster analysis and Image retrieval.
As part of his studies on Information retrieval, Wei-Ying Ma often connects relevant subjects like Web page.
His work deals with themes such as Natural language processing, Computer vision, Function, Machine learning and Pattern recognition, which intersect with Artificial intelligence.
His Data mining study incorporates themes from Consistency, Scalability and Graph.
His work carried out in the field of Cluster analysis brings together such families of science as Search engine indexing and Feature vector.
Wei-Ying Ma combines subjects such as Image processing and Feature extraction with his study of Image retrieval.
His most cited work include:
- Texture features for browsing and retrieval of image data (3401 citations)
- Mining interesting locations and travel sequences from GPS trajectories (1426 citations)
- A survey of content-based image retrieval with high-level semantics (1366 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Information retrieval, Artificial intelligence, Data mining, World Wide Web and Web page.
His research ties Cluster analysis and Information retrieval together.
His work in Artificial intelligence addresses issues such as Pattern recognition, which are connected to fields such as Feature.
His Data mining study combines topics in areas such as Graph, Link analysis and Feature vector.
His Web search query study combines topics from a wide range of disciplines, such as Query expansion and Semantic search.
His Image retrieval study typically links adjacent topics like Image texture.
He most often published in these fields:
- Information retrieval (39.54%)
- Artificial intelligence (32.85%)
- Data mining (22.80%)
What were the highlights of his more recent work (between 2009-2020)?
- Artificial intelligence (32.85%)
- Information retrieval (39.54%)
- World Wide Web (18.20%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Information retrieval, World Wide Web, Machine learning and Natural language processing.
The various areas that he examines in his Artificial intelligence study include Computer vision and Pattern recognition.
His study on Semantic search, Search analytics, Web search query and Web query classification is often connected to Source system as part of broader study in Information retrieval.
His World Wide Web research integrates issues from Key and Global Positioning System.
His studies in Machine learning integrate themes in fields like Probabilistic logic and Web page.
As part of the same scientific family, Wei-Ying Ma usually focuses on Image retrieval, concentrating on Feature extraction and intersecting with Visualization and Annotation.
Between 2009 and 2020, his most popular works were:
- GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory. (753 citations)
- Collaborative Knowledge Base Embedding for Recommender Systems (553 citations)
- Recommending friends and locations based on individual location history (399 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Wei-Ying Ma focuses on Artificial intelligence, Context, Conversation, Natural language processing and Speech recognition.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Tree structure, Machine learning, Segment tree and Pattern recognition.
His biological study spans a wide range of topics, including Topic model, Scale, Computer cluster, Theoretical computer science and Vocabulary.
His study in the field of Sentence also crosses realms of Mechanism.
He works mostly in the field of Annotation, limiting it down to topics relating to Image retrieval and, in certain cases, Information retrieval.
His research integrates issues of Object, Feature extraction, Point and Automatic image annotation in his study of Information retrieval.
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