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» Pattern Recognition Using Functions of Multiple Instances
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KDD
2004
ACM
135views Data Mining» more  KDD 2004»
16 years 5 months ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that driv...
Giles Hooker
GECCO
2008
Springer
141views Optimization» more  GECCO 2008»
15 years 5 months ago
Managing team-based problem solving with symbiotic bid-based genetic programming
Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an a priori specification of the number of team member...
Peter Lichodzijewski, Malcolm I. Heywood
CVPR
2012
IEEE
13 years 7 months ago
Social interactions: A first-person perspective
This paper presents a method for the detection and recognition of social interactions in a day-long first-person video of a social event, like a trip to an amusement park. The lo...
Alireza Fathi, Jessica K. Hodgins, James M. Rehg
CVPR
2009
IEEE
16 years 2 months ago
A projector-camera setup for geometry-invariant frequency demultiplexing
Consider a projector-camera setup where a sinusoidal pattern is projected onto the scene, and an image of the objects imprinted with the pattern is captured by the camera. In this...
Daniel A. Vaquero, Matthew Turk, Ramesh Raskar, Ro...
IDA
2005
Springer
15 years 10 months ago
Removing Statistical Biases in Unsupervised Sequence Learning
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize the...
Yoav Horman, Gal A. Kaminka