Tin Kam Ho
Tin Kam Ho (何天琴) is a computer scientist at IBM Watson with contributions to machine learning, data mining, and classification. Ho is noted for introducing random decision forests in 1995, and for her pioneering work in ensemble learning and data complexity analysis. She is an IEEE fellow and IAPR fellow.
Career and research
Since 2014 Tin Kam Ho has been a research scientist in Artificial Intelligence at IBM. Previously she led the Statistics and Learning Research Department of Bell Labs at Murray Hill, NJ. She pioneered research in multiple classifier systems, random decision forests, and data complexity analysis, and pursued applications of automatic learning in reading systems and many areas of science and engineering. She also led major efforts on modeling and monitoring large-scale optical transmission systems. Later she worked on wireless geo-location, video surveillance, smart grid data mining, user profiling, customer experience modeling, and analysis of diagnostic processes. Her contributions were recognized by a Bell Labs President's Gold Award and two Bell Labs Teamwork Awards, a Young Scientist Award from ICDAR in 1999, and the 2008 Pierre Devijver Award for Statistical Pattern Recognition. She served as Editor-in-chief of the journal Pattern Recognition Letters in 2004-2010, and in earlier years as Associate Editor for PAMI, Pattern Recognition, Editor for Int. J. on Document Analysis and Recognition, as well as guest editors for other publications. 
- "Tin Kam Ho - IBM". researcher.watson.ibm.com. 2016-07-25. Retrieved 2018-01-29.
- Ho, Tin Kam (August 1995). "Random decision forests". Proceedings of 3rd International Conference on Document Analysis and Recognition. 1: 278–282 vol.1. doi:10.1109/ICDAR.1995.598994. ISBN 978-0-8186-7128-9.
- Ho, Tin Kam (1992). "A theory of multiple classifier systems and its application to visual word recognition". Cite journal requires
- Ho, Tin Kam (January 2014). "Knowledge from patterns: from the mail sorting facility to a world of connected devices" (PDF). IAPR Newsletter. 36:1: 5–7.
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