1997 - IEEE Fellow For contributions to the theory and practice of neural networks.
His main research concerns Artificial intelligence, Information retrieval, World Wide Web, Artificial neural network and Machine learning. His biological study spans a wide range of topics, including Natural language processing and Pattern recognition. His study connects Citation and Information retrieval.
His work on Search engine, Web development, Web standards and Web analytics as part of general World Wide Web research is frequently linked to Scientific literature, thereby connecting diverse disciplines of science. The various areas that C. Lee Giles examines in his Search engine study include Web page, Focused crawler and Index. His Artificial neural network study deals with Finite-state machine intersecting with Connectionism.
The scientist’s investigation covers issues in Information retrieval, Artificial intelligence, World Wide Web, Search engine and Machine learning. His Information retrieval study incorporates themes from Metadata and Data mining. His studies in Artificial intelligence integrate themes in fields like Pattern recognition and Natural language processing.
His World Wide Web study frequently draws connections between related disciplines such as Data science. His Search engine study frequently involves adjacent topics like Web page. His research in Recurrent neural network intersects with topics in Finite-state machine and Time delay neural network.
His primary scientific interests are in Artificial intelligence, Information retrieval, Machine learning, Recurrent neural network and Task. C. Lee Giles interconnects Pattern recognition and Natural language processing in the investigation of issues within Artificial intelligence. C. Lee Giles focuses mostly in the field of Information retrieval, narrowing it down to topics relating to Metadata and, in certain cases, Big data.
His studies in Big data integrate themes in fields like Scalability and World Wide Web. C. Lee Giles has included themes like Adversarial system, Neural coding and Robustness in his Machine learning study. His research on Recurrent neural network concerns the broader Artificial neural network.
His primary areas of investigation include Artificial intelligence, Machine learning, Recurrent neural network, Pattern recognition and Adversarial system. His work carried out in the field of Artificial intelligence brings together such families of science as Task and Natural language processing. His Machine learning study integrates concerns from other disciplines, such as Concept map, Identification and Knowledge graph.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Graph and Text detection. C. Lee Giles combines subjects such as Artificial neural network, Deep neural networks, Regularization and Robustness with his study of Adversarial system. Construct and Information retrieval are two areas of study in which C. Lee Giles engages in interdisciplinary research.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Accessibility of information on the Web
Steve Lawrence;C. Lee Giles.
Searching the World Wide Web
Steve Lawrence;C. Lee Giles.
Efficient identification of Web communities
Gary William Flake;Steve Lawrence;C. Lee Giles.
knowledge discovery and data mining (2000)
Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping
Rich Caruana;Steve Lawrence;C. Lee Giles.
neural information processing systems (2000)
Digital libraries and autonomous citation indexing
S. Lawrence;C. Lee Giles;K. Bollacker.
IEEE Computer (1999)
CiteSeer: an automatic citation indexing system
C. Lee Giles;Kurt D. Bollacker;Steve Lawrence.
acm international conference on digital libraries (1998)
Focused Crawling Using Context Graphs
Michelangelo Diligenti;Frans Coetzee;Steve Lawrence;C. Lee Giles.
very large data bases (2000)
Learning, invariance, and generalization in high-order neural networks
C. Lee Giles;Tom Maxwell.
Applied Optics (1987)
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
David M. Pennock;Eric Horvitz;Steve Lawrence;C. Lee Giles.
uncertainty in artificial intelligence (2000)
Winners don't take all: Characterizing the competition for links on the web
David M. Pennock;Gary William Flake;Steve Lawrence;Eric J. Glover.
Proceedings of the National Academy of Sciences of the United States of America (2002)
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