.png){kind=icon}
Return
Active sampling for multiple output identification
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-007-5026-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We study functions with multiple output values, and use active sampling to identify an example for each of the possible output values. Our results for this setting include: (1) Efficient active sampling algorithms for simple geometric concepts, such as intervals on a line and axis parallel boxes. (2) A characterization for the case of binary output value in a transductive setting. (3) An analysis of active sampling with uniform distribution in the plane. (4) An efficient algorithm for the Boolean hypercube when each output value is a monomial.
Keywords:
active learning
active sampling
hitting
VC dimension
transductive learning
output identification
separation dimension
Journal
IF:
2.9
Papers:
2.7K
Citations:
3.4W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveUsing the youngest asteroid clusters to constrain the space weathering and gardening rate on S-complex asteroids.png)
Icarus
OAAI
IcarusIF0
OAAIShareSaveShareSave
View more