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CS 170 INTRO TO ARTIFICIAL INTELLIGENCE CS 170 INTRO TO ARTIFICIAL INTELLIGENCE Dr Eamonn Keogh eamonn@cs.uci.edu eamonn/ Office:

CS 170 INTRO TO ARTIFICIAL INTELLIGENCE CS 170 INTRO TO ARTIFICIAL INTELLIGENCE Dr Eamonn Keogh [email protected] eamonn/ Office:

Eamonn Keogh - Computer Science and Engineering: Welcome to CSE @ UCR

Eamonn Keogh - Computer Science and Engineering: Welcome to CSE @ UCR

Using Computers to Understand (and control) Insects Eamonn Keogh Computer Science & Engineering Department University of California - Riverside Riverside,

Using Computers to Understand (and control) Insects Eamonn Keogh Computer Science & Engineering Department University of California - Riverside Riverside,

Symbolic Representations of Time Series Eamonn Keogh and Jessica Lin

Symbolic Representations of Time Series Eamonn Keogh and Jessica Lin

Dendrograms for Data Mining Eamonn Keogh. Informally, finding natural groupings or relationships among objects. What is Clustering? Also called unsupervised.

Dendrograms for Data Mining Eamonn Keogh. Informally, finding natural groupings or relationships among objects. What is Clustering? Also called unsupervised.

Classification Dr Eamonn Keogh Computer Science & Engineering Department University of California - Riverside Riverside,CA 92521 eamonn@cs.ucr.edu Who.

Classification Dr Eamonn Keogh Computer Science & Engineering Department University of California - Riverside Riverside,CA 92521 [email protected] Who.

Using Relevance Feedback in Multimedia Databases Chotirat “Ann” Ratanamahatana Eamonn Keogh 7 th International Conference on VISual Information Systems.

Using Relevance Feedback in Multimedia Databases Chotirat “Ann” Ratanamahatana Eamonn Keogh 7 th International Conference on VISual Information Systems.

CS 235 Data Mining Winter 2011 Eamonn Keogh UCR TA: Abdullah Mueen.

CS 235 Data Mining Winter 2011 Eamonn Keogh UCR TA: Abdullah Mueen.

Mining Time Series. (c) Eamonn Keogh, eamonn@cs.ucr.edu 2 Why is Working With Time Series so Difficult? Part I 1 Hour of ECG data: w 1 Hour of ECG data:

Mining Time Series. (c) Eamonn Keogh, [email protected] 2 Why is Working With Time Series so Difficult? Part I 1 Hour of ECG data: w 1 Hour of ECG data:

Abdullah Mueen Eamonn Keogh University of California, Riverside

Abdullah Mueen Eamonn Keogh University of California, Riverside

SAX! A Symbolic Representations of Time Series Eamonn Keogh and Jessica Lin

SAX! A Symbolic Representations of Time Series Eamonn Keogh and Jessica Lin

Exact indexing of Dynamic Time Warping Presented By: Ankit Hirdesh Piyush Goswami Eamonn Keogh Computer Science & Engineering Department University of.

Exact indexing of Dynamic Time Warping Presented By: Ankit Hirdesh Piyush Goswami Eamonn Keogh Computer Science & Engineering Department University of.

SAX: a Novel Symbolic Representation of Time Series Authors Jessica Lin Eamonn Keogh Li Wei Stefano Lonardi Presenter Arif Bin Hossain Slides incorporate.

SAX: a Novel Symbolic Representation of Time Series Authors Jessica Lin Eamonn Keogh Li Wei Stefano Lonardi Presenter Arif Bin Hossain Slides incorporate.

Eamonn Keogh What can Computer Science do for Malaria Research? Computer Science & Engineering Department University of California - Riverside Riverside,

Eamonn Keogh What can Computer Science do for Malaria Research? Computer Science & Engineering Department University of California - Riverside Riverside,

Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy Nurjahan BegumLiudmila Ulanova Jun Wang 1 Eamonn Keogh University.

Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy Nurjahan BegumLiudmila Ulanova Jun Wang 1 Eamonn Keogh University.

Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data Thanawin Rakthanmanon Eamonn Keogh Stefano Lonardi Scott Evans.

Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data Thanawin Rakthanmanon Eamonn Keogh Stefano Lonardi Scott Evans.

Spatial Access Methods Chapter 26 of book Read only 26.1, 26.2, 26.6 Dr Eamonn Keogh

Spatial Access Methods Chapter 26 of book Read only 26.1, 26.2, 26.6 Dr Eamonn Keogh

Magic Camera Master’s Project Defense By Adam Meadows Project Committee: Dr. Eamonn Keogh Dr. Doug Tolbert.

Magic Camera Master’s Project Defense By Adam Meadows Project Committee: Dr. Eamonn Keogh Dr. Doug Tolbert.

Accelerating the Dynamic Time Warping Distance Measure Using Logarithmic Arithmetic Joseph Tarango, Eamonn Keogh, Philip Brisk {jtarango,eamonn,philip}@cs.ucr.edu.

Accelerating the Dynamic Time Warping Distance Measure Using Logarithmic Arithmetic Joseph Tarango, Eamonn Keogh, Philip Brisk {jtarango,eamonn,philip}@cs.ucr.edu.

UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL ...mpechen/courses/TIES443/handouts/lecture… · (c) Eamonn Keogh, eamonn@cs.ucr.edu UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF

UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL ...mpechen/courses/TIES443/handouts/lecture… · (c) Eamonn Keogh, [email protected] UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF