Sciweavers

COLT
2001
Springer
14 years 4 months ago
Intrinsic Complexity of Learning Geometrical Concepts from Positive Data
Intrinsic complexity is used to measure the complexity of learning areas limited by broken-straight lines (called open semi-hulls) and intersections of such areas. Any strategy le...
Sanjay Jain, Efim B. Kinber
COLT
2001
Springer
14 years 4 months ago
Robust Learning - Rich and Poor
A class C of recursive functions is called robustly learnable in the sense I (where I is any success criterion of learning) if not only C itself but even all transformed classes Î...
John Case, Sanjay Jain, Frank Stephan, Rolf Wiehag...
COLT
2001
Springer
14 years 4 months ago
On the Synthesis of Strategies Identifying Recursive Functions
A classical learning problem in Inductive Inference consists of identifying each function of a given class of recursive functions from a ï¬nite number of its output values. Unifor...
Sandra Zilles
COLT
2001
Springer
14 years 4 months ago
On Learning Monotone DNF under Product Distributions
We show that the class of monotone 2O( √ log n)-term DNF formulae can be PAC learned in polynomial time under the uniform distribution from random examples only. This is an expo...
Rocco A. Servedio
COLT
2001
Springer
14 years 4 months ago
Smooth Boosting and Learning with Malicious Noise
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 ...
Rocco A. Servedio
COLT
2001
Springer
14 years 4 months ago
A Generalized Representer Theorem
Bernhard Schölkopf, Ralf Herbrich, Alex J. Sm...
COLT
2001
Springer
14 years 4 months ago
Geometric Bounds for Generalization in Boosting
We consider geometric conditions on a labeled data set which guarantee that boosting algorithms work well when linear classiï¬ers are used as weak learners. We start by providing ...
Shie Mannor, Ron Meir
COLT
2001
Springer
14 years 4 months ago
Learning Additive Models Online with Fast Evaluating Kernels
Abstract. We develop three new techniques to build on the recent advances in online learning with kernels. First, we show that an exponential speed-up in prediction time per trial ...
Mark Herbster