9 search results - page 1 / 2 » On PAC Learning Using Winnow, Perceptron, and a Perceptron-l... |
COLT
1999
Springer
13 years 9 months ago
1999
Springer
In this paper we analyze the PAC learning abilities of several simple iterative algorithms for learning linear threshold functions, obtaining both positive and negative results. W...
COLT
2000
Springer
13 years 9 months ago
2000
Springer
We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds...
COLT
2001
Springer
13 years 9 months ago
2001
Springer
We describe a new boosting algorithm which generates only smooth distributions which do not assign too much weight to any single example. We show that this new boosting algorithm ...
NIPS
2000
13 years 6 months ago
2000
In theory, the Winnow multiplicative update has certain advantages over the Perceptron additive update when there are many irrelevant attributes. Recently, there has been much eff...
COLT
1997
Springer
13 years 8 months ago
1997
Springer
The problem of learning linear discriminant concepts can be solved by various mistake-driven update procedures, including the Winnow family of algorithms and the well-known Percep...