The scientist’s investigation covers issues in Information retrieval, Artificial intelligence, Data mining, Machine learning and Question answering. His study in the field of Relevance and Search engine is also linked to topics like Forum spam and Filter. His Relevance research is multidisciplinary, relying on both Ranking and Measure.
Gordon V. Cormack combines subjects such as Construct and Active learning with his study of Artificial intelligence. His study in the field of Ranking also crosses realms of Software deployment, Function and Recall. Gordon V. Cormack interconnects Ambiguity, Selection, Redundancy and Multiple choice in the investigation of issues within Question answering.
Gordon V. Cormack mainly investigates Information retrieval, Artificial intelligence, Recall, Machine learning and Data mining. His work on Relevance, Ranking and Search engine as part of general Information retrieval study is frequently connected to Track and Relevance feedback, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His work deals with themes such as Sampling, Query expansion, Test and Pooling, which intersect with Relevance.
As a part of the same scientific family, Gordon V. Cormack mostly works in the field of Ranking, focusing on Okapi BM25 and, on occasion, Ranking SVM. His Artificial intelligence research incorporates themes from Pattern recognition and Natural language processing. He has included themes like Gold standard and Programming language in his Natural language processing study.
His primary areas of investigation include Information retrieval, Recall, Relevance, Track and Active learning. His biological study spans a wide range of topics, including Sentiment analysis, Training set, Property, Social media and Coding. His Relevance study combines topics in areas such as Test, Pooling, Ideal, Selection and Sampling.
His Active learning study incorporates themes from Search engine, Classifier, Artificial intelligence, Multimedia and Machine learning. His research investigates the connection between Artificial intelligence and topics such as Natural language processing that intersect with issues in Pattern recognition. His studies deal with areas such as Time complexity and Residual as well as Machine learning.
Information retrieval, Recall, Relevance feedback, Relevance and Active learning are his primary areas of study. In his research, Gordon V. Cormack undertakes multidisciplinary study on Information retrieval and Track. The Relevance study combines topics in areas such as Test, Ideal, Precision and recall, Property and NIST.
His biological study deals with issues like Contrast, which deal with fields such as Artificial intelligence. His study looks at the intersection of Active learning and topics like Search engine with Active learning, Interface and Flexibility. The concepts of his Machine learning study are interwoven with issues in Classifier, Scalability, Data mining and Time complexity.
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.
Novelty and diversity in information retrieval evaluation
Charles L.A. Clarke;Maheedhar Kolla;Gordon V. Cormack;Olga Vechtomova.
international acm sigir conference on research and development in information retrieval (2008)
Information Retrieval: Implementing and Evaluating Search Engines
Stefan Büttcher;Charles Clarke;Gordon V. Cormack.
Data compression using dynamic Markov modelling
G. V. Cormack;R. N. S. Horspool.
The Computer Journal (1987)
Email Spam Filtering: A Systematic Review
Gordon V. Cormack.
Spam Filtering Using Statistical Data Compression Models
Andrej Bratko;Bogdan Filipič;Gordon V. Cormack;Thomas R. Lynam.
Journal of Machine Learning Research (2006)
Efficient and effective spam filtering and re-ranking for large web datasets
Gordon V. Cormack;Mark D. Smucker;Charles L. Clarke.
Information Retrieval (2011)
Exploiting redundancy in question answering
Charles L. A. Clarke;Gordon V. Cormack;Thomas R. Lynam.
international acm sigir conference on research and development in information retrieval (2001)
Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Gordon V. Cormack;Charles L A Clarke;Stefan Buettcher.
international acm sigir conference on research and development in information retrieval (2009)
TREC 2005 Spam Track Overview
Gordon V. Cormack;Thomas R. Lynam.
text retrieval conference (2005)
Relevance ranking for one to three term queries
Charles L. A. Clarke;Gordon V. Cormack;Elizabeth A. Tudhope.
Information Processing and Management (2000)
Profile was last updated on December 6th, 2021.
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