Sciweavers
Explore
Publications
Books
Software
Tutorials
Presentations
Lectures Notes
Datasets
Labs
Conferences
Community
Upcoming
Conferences
Top Ranked Papers
Most Viewed Conferences
Conferences by Acronym
Conferences by Subject
Conferences by Year
Tools
Sci2ools
International Keyboard
Graphical Social Symbols
CSS3 Style Generator
OCR
Web Page to Image
Web Page to PDF
Merge PDF
Split PDF
Latex Equation Editor
Extract Images from PDF
Convert JPEG to PS
Convert Latex to Word
Convert Word to PDF
Image Converter
PDF Converter
Community
Sciweavers
About
Terms of Use
Privacy Policy
Cookies
Free Online Productivity Tools
i2Speak
i2Symbol
i2OCR
iTex2Img
iWeb2Print
iWeb2Shot
i2Type
iPdf2Split
iPdf2Merge
i2Bopomofo
i2Arabic
i2Style
i2Image
i2PDF
iLatex2Rtf
Sci2ools
36
click to vote
JMLR
2010
favorite
Email
discuss
report
82
views
more
JMLR 2010
»
Negative Results for Active Learning with Convex Losses
13 years 7 months ago
Download
www.cs.cmu.edu
We study the problem of active learning with convex loss functions. We prove that even under bounded noise constraints, the minimax rates for proper active learning are often no better than passive learning.
Steve Hanneke, Liu Yang
Real-time Traffic
Convex Loss Functions
|
JMLR 2010
|
Passive Learning
|
Proper Active Learning
|
claim paper
Post Info
More Details (n/a)
Added
19 May 2011
Updated
19 May 2011
Type
Journal
Year
2010
Where
JMLR
Authors
Steve Hanneke, Liu Yang
Comments
(0)
Researcher Info
JMLR 2000 Study Group
Computer Vision