BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/North_Dakota/Center BEGIN:DAYLIGHT DTSTART:20120311T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20121104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20120626T133752Z UID:EF4DE021-E5B6-11E7-833E-0050568D7F66 DTSTART;TZID=America/North_Dakota/Center:20120506T140000 DTEND;TZID=America/North_Dakota/Center:20120506T160000 DESCRIPTION:Seminar by Prof. Don Wunsh\, BARTMAP vs. Iterative Two-Way Clus tering\, and Other Innovations Abstract: The unsupervised nature of many c lustering algorithms is one of their key advantages\, even though many des ign decisions must still be made. In discussing some of these design decis ions\, we will briefly survey advantages and design issues in hierarchical clustering. Similarly\, we will review some properties of Adaptive Resona nce in engineering applications of clustering. This will lead us to an inn ovative application of an ART-inspired architecture (BARTMAP) to bicluster ing\, an unsupervised version of heteroassociative learning. Comparison of this approach to other biclustering and traditional clustering approaches illustrates the advantages of biclustering in general and BARTMAP in part icular. This advantage is further extended by the development of a new\, h ierarchical version of BARTMAP. Donald Wunsch is the Mary K. Finley Missou ri Distinguished Professor at Missouri University of Science & Technology (Missouri S&T). Earlier employers were: Texas Tech University\, Boeing\, R ockwell International\, and International Laser Systems. His education inc ludes: Executive MBA - Washington University in St. Louis\, Ph.D.\, Electr ical Engineering - University of Washington (Seattle)\, M.S.\, Applied Mat hematics (same institution)\, B.S.\, Applied Mathematics - University of N ew Mexico. Key research contributions are: Clustering\; Adaptive resonance and Reinforcement Learning architectures\, hardware and applications\; Ne urofuzzy regression\; Traveling Salesman Problem heuristics\; Robotic Swar ms\; and Bioinformatics. He has produced 16 Ph.D. recipients in Computer E ngineering\, Electrical Engineering\, and Computer Science\; has attracted over $8 million in sponsored research\; and has over 300 publications inc luding nine books. His research has been cited over 4500 times. He is an I EEE Fellow and previous INNS President\, INNS Fellow and Senior Fellow 07 - present\, IEEE Electron Devices Society Distinguished Lecturer\, and ser ved as IJCNN General Chair\, and on several Boards\, including the St. Pat rick's School Board\, IEEE Neural Networks Council\, International Neural Networks Society\, and the University of Missouri Bioinformatics Consortiu m. He chairs the Missouri S&T Information Technology and Computing Committ ee\, a Faculty Senate Standing Committee.\n\nColumbia\, Missouri\, United States LOCATION:Columbia\, Missouri\, United States ORGANIZER: SEQUENCE:0 SUMMARY:[Legacy Report] Technical Seminar URL;VALUE=URI:https://events.vtools.ieee.org/m/76761 X-ALT-DESC:Description: <br />Seminar by Prof. Don Wunsh\, \n\nBARTMAP vs. Iterative Two-Way Clustering\, and Other Innovations\n\nAbstract: The unsu pervised nature of many clustering algorithms is one of their key advantag es\, even though many design decisions must still be made. In discussing s ome of these design decisions\, we will briefly survey advantages and desi gn issues in hierarchical clustering. Similarly\, we will review some prop erties of Adaptive Resonance in engineering applications of clustering. Th is will lead us to an innovative application of an ART-inspired architectu re (BARTMAP) to biclustering\, an unsupervised version of heteroassociativ e learning. Comparison of this approach to other biclustering and traditio nal clustering approaches illustrates the advantages of biclustering in ge neral and BARTMAP in particular. This advantage is further extended by the development of a new\, hierarchical version of BARTMAP. \n\nDonald Wunsch is the Mary K. Finley Missouri Distinguished Professor at Missouri Univer sity of Science & Technology (Missouri S&T). Earlier employers were: Texas Tech University\, Boeing\, Rockwell International\, and International Las er Systems. His education includes: Executive MBA - Washington University in St. Louis\, Ph.D.\, Electrical Engineering - University of Washington ( Seattle)\, M.S.\, Applied Mathematics (same institution)\, B.S.\, Applied Mathematics - University of New Mexico. Key research contributions are: Cl ustering\; Adaptive resonance and Reinforcement Learning architectures\, h ardware and applications\; Neurofuzzy regression\; Traveling Salesman Prob lem heuristics\; Robotic Swarms\; and Bioinformatics. He has produced 16 P h.D. recipients in Computer Engineering\, Electrical Engineering\, and Com puter Science\; has attracted over $8 million in sponsored research\; and has over 300 publications including nine books. His research has been cite d over 4500 times. He is an IEEE Fellow and previous INNS President\, INNS Fellow and Senior Fellow 07 - present\, IEEE Electron Devices Society Dis tinguished Lecturer\, and served as IJCNN General Chair\, and on several B oards\, including the St. Patrick's School Board\, IEEE Neural Networks Co uncil\, International Neural Networks Society\, and the University of Miss ouri Bioinformatics Consortium. He chairs the Missouri S&T Information Tec hnology and Computing Committee\, a Faculty Senate Standing Committee. END:VEVENT END:VCALENDAR