1997 - Fellow of the Royal Society of New Zealand
1996 - ACM Fellow For contributions to the study of how past behavior can expedite future interaction, in particular adaptive data compression, programming by demonstration, and machine learning.
Ian H. Witten mostly deals with Artificial intelligence, Machine learning, Information retrieval, World Wide Web and Data mining. The various areas that Ian H. Witten examines in his Artificial intelligence study include Pattern recognition and Natural language processing. In the field of Machine learning, his study on Active learning and Decision tree learning overlaps with subjects such as Workbench and Generalization.
When carried out as part of a general Active learning research project, his work on Instance-based learning is frequently linked to work in Hyper-heuristic, therefore connecting diverse disciplines of study. His World Wide Web study integrates concerns from other disciplines, such as Text mining, Ontology, Software and Semantics. His studies deal with areas such as Graphical user interface, Set, Rule sets and Data science as well as Data mining.
Ian H. Witten focuses on World Wide Web, Artificial intelligence, Information retrieval, Machine learning and Multimedia. His World Wide Web study combines topics from a wide range of disciplines, such as User interface, Software and Interface. His biological study spans a wide range of topics, including Pattern recognition, Data mining, Computer vision and Natural language processing.
His Information retrieval research is multidisciplinary, relying on both Document clustering and Index. Machine learning is represented through his Instance-based learning, Decision tree and Active learning research. His Data compression research is under the purview of Algorithm.
The scientist’s investigation covers issues in World Wide Web, Artificial intelligence, Information retrieval, Software and Machine learning. His World Wide Web study combines topics in areas such as User interface, Multimedia, Resource and Interface. The study incorporates disciplines such as Key and Natural language processing in addition to Artificial intelligence.
His studies in Information retrieval integrate themes in fields like Hyperlink, Index and Document clustering. In the subject of general Software, his work in Open source software and Software system is often linked to Workbench, thereby combining diverse domains of study. His research investigates the link between Machine learning and topics such as Data mining that cross with problems in Field.
Ian H. Witten mainly focuses on Artificial intelligence, Information retrieval, World Wide Web, Software and Semantic similarity. Ian H. Witten interconnects IBM PC compatible, Machine learning, Key and Natural language processing in the investigation of issues within Artificial intelligence. His Machine learning research includes elements of Algorithm and Data mining.
Ian H. Witten combines subjects such as Domain, Structure and Cluster analysis with his study of Information retrieval. The concepts of his World Wide Web study are interwoven with issues in Ontology, User interface and Multimedia. His research integrates issues of C4.5 algorithm, DSPACE and Stress testing in his study of Software.
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.
Data mining: practical machine learning tools and techniques with Java implementations
Ian H. Witten;Eibe Frank.
international conference on management of data (2002)
Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten;Eibe Frank;Mark A. Hall.
The WEKA data mining software: an update
Mark Hall;Eibe Frank;Geoffrey Holmes;Bernhard Pfahringer.
Sigkdd Explorations (2009)
Arithmetic coding for data compression
Ian H. Witten;Radford M. Neal;John G. Cleary.
Communications of The ACM (1987)
Managing Gigabytes: Compressing and Indexing Documents and Images
I.H. Witten;A. Moffat;T.C. Bell.
Timothy C. Bell;John G. Cleary;Ian H. Witten.
Generating Accurate Rule Sets Without Global Optimization
Eibe Frank;Ian H. Witten.
international conference on machine learning (1998)
Data Compression Using Adaptive Coding and Partial String Matching
J. Cleary;I. Witten.
IEEE Transactions on Communications (1984)
Learning to link with wikipedia
David Milne;Ian H. Witten.
conference on information and knowledge management (2008)
Induction of model trees for predicting continuous classes
Yong Wang;Ian H. Witten.
Profile was last updated on December 6th, 2021.
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