1 / 4

Breakout Session (B2) Machine Learning, Data Mining and Behavior Modeling

Breakout Session (B2) Machine Learning, Data Mining and Behavior Modeling. Moderators: Narayanan C Krishnan, WSU and Qiang Yang, Hong Kong UST. B2 Machine learning/behavior modeling, data mining . Facilitators/Scribes:

kamal
Télécharger la présentation

Breakout Session (B2) Machine Learning, Data Mining and Behavior Modeling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Breakout Session (B2)Machine Learning, Data Mining and Behavior Modeling Moderators: Narayanan C Krishnan, WSU and Qiang Yang, Hong Kong UST

  2. B2 Machine learning/behavior modeling, data mining • Facilitators/Scribes: • Narayanan Krishnan (Washington State University), Qiang Yang (Hong Kong University of Science and Technology, Hong Kong) • Attendees: • Du Li, David Chu, Gustavo de Veciana, Dejing Dou, Diane Cook, FarnoushBanaei-Kashani, MircoMusolesi, Oliver Brdiczka, Wang-Chien Lee, TanzeemChoudhury, Wendy Nilsen, Michael Anderson, James Landay, James Rehg, Mohan Trivedi, SvethaVenkatesh, Andrew Campbell

  3. Central Questions • Challenges in • Data • Algorithms • Interdisciplinary

  4. Machine Learning and Data Mining, Behavior Modeling • Grand Challenges • Data • Share the data, annotate the data socially, where the data meet the objectives, • Longitudinal data set over time • Social media for data collection and annotation • Algorithms • Live with noise, poorly labeled but large quantities of data, but design algorithms that are distributed, adaptive, capable of online learning (can learn as the data arrives) • Benchmarking, competition • Multidisciplinary • Better understanding of the application goals and objectives • Understanding taxonomy, temporal, social properties human behavior • Education programs • Funding for Interdisciplinary research for behavior modeling

More Related