Sciweavers

7924 search results - page 1183 / 1585
» Non-Malleable Functions and Their Applications
Sort
View
ICML
2004
IEEE
16 years 7 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
ICML
2003
IEEE
16 years 7 months ago
Incorporating Diversity in Active Learning with Support Vector Machines
In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Differ...
Klaus Brinker
ICML
1998
IEEE
16 years 7 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
ICSE
2009
IEEE-ACM
16 years 7 months ago
Automatically finding patches using genetic programming
Automatic repair of programs has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locati...
Westley Weimer, ThanhVu Nguyen, Claire Le Goues, S...
ICSE
2008
IEEE-ACM
16 years 7 months ago
Time-bounded adaptation for automotive system software
Software is increasingly deployed in vehicles as demand for new functionality increases and cheaper and more powerful hardware becomes available. Likewise, emerging wireless commu...
Aline Senart, Douglas C. Schmidt, Serena Fritsch, ...
« Prev « First page 1183 / 1585 Last » Next »