M
Michael J. Pazzani
Researcher at University of California, Riverside
Publications - 190
Citations - 29519
Michael J. Pazzani is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Explanation-based learning & Stability (learning theory). The author has an hindex of 62, co-authored 183 publications receiving 28036 citations. Previous affiliations of Michael J. Pazzani include University of California & Rutgers University.
Papers
More filters
Journal ArticleDOI
Learning with globally predictive tests
TL;DR: A new bias is introduced for rule learning systems that allows a rule learner to create a rule that predicts class membership if each test of the rule in isolation is predictive of that class.
Book ChapterDOI
Theory-guided concept formation
Doug Fisher,Michael J. Pazzani +1 more
TL;DR: The explanation-based and case-based paradigms provide some guidance on how inference, categorization, and learning interact, though considerable research remains to be done before the field realizes a robust coupling of these processes within a single model.
Book ChapterDOI
Commercial Applications of Machine Learning for Personalized Wireless Portals
TL;DR: In this paper, the authors summarize commercially deployed systems using machine learning methods for personalizing mobile information delivery, including agents that learn user's preferences and select information for the user, which is a convenience when displaying information on a 19-inch desktop monitor accessed over a broadband connection.
Posted Content
A Natural Language Query Interface for Searching Personal Information on Smartwatches
TL;DR: In this article, a light-weight natural language based query interface, including a text parser algorithm and a user interface, was designed to operate on small devices, i.e. smartwatches, as well as augmenting the personal assistant systems by allowing them to process end users' natural language queries about their quantified-self data.