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
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Proceedings Article
Syskill & webert: Identifying interesting web sites
TL;DR: The naive Bayesian classifier offers several advantages over other learning algorithms on this task and an initial portion of a web page is sufficient for making predictions on its interestingness substantially reducing the amount of network transmission required to make predictions.
Proceedings ArticleDOI
Scaling up dynamic time warping for datamining applications
Eamonn Keogh,Michael J. Pazzani +1 more
TL;DR: This paper introduces a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate Approximation (PAA) which allows us to outperform DTW by one to two orders of magnitude, with no loss of accuracy.
Segmenting Time Series: A Survey and Novel Approach
TL;DR: This paper undertake the first extensive review and empirical comparison of all proposed techniques for mining time series data and introduces a novel algorithm that is empirically show to be superior to all others in the literature.
Journal ArticleDOI
Locally adaptive dimensionality reduction for indexing large time series databases
TL;DR: This article introduces a new dimensionality reduction technique, which it is shown how APCA can be indexed using a multidimensional index structure, and proposes two distance measures in the indexed space that exploit the high fidelity of APCA for fast searching.
Proceedings Article
An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback
Eamonn Keogh,Michael J. Pazzani +1 more
TL;DR: An extended representation of time series that allows fast, accurate classification and clustering in addition to the ability to explore time series data in a relevance feedback framework is introduced.