COLT
2005
Springer
13 years 10 months ago
2005
Springer
Abstract. We present several results related to ranking. We give a general margin-based bound for ranking based on the L∞ covering number of the hypothesis space. Our bound sugge...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
In recent work Long and Servedio [LS05] presented a “martingale boosting” algorithm that works by constructing a branching program over weak classifiers and has a simple anal...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
We present a general decomposition algorithm that is uniformly applicable to every (suitably normalized) instance of Convex Quadratic Optimization and efficiently approaches an o...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
We present a new generalization bound where the use of unlabeled examples results in a better ratio between training-set size and the resulting classifier’s quality and thus red...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
COLT
2005
Springer
13 years 10 months ago
2005
Springer
We give a compression scheme for any maximum class of VC dimension d that compresses any sample consistent with a concept in the class to at most d unlabeled points from the domain...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
We examine a Markovian model for the price evolution of a stock, in which the probability of local upward or downward movement is arbitrarily dependent on the current price itself...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
We present two new methods for obtaining generalization error bounds in a semi-supervised setting. Both methods are based on approximating the disagreement probability of pairs of ...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
In the machine learning community it is generally believed that graph Laplacians corresponding to a finite sample of data points converge to a continuous Laplace operator if the s...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
We start by showing that in an active learning setting, the Perceptron algorithm needs Ω( 1 ε2 ) labels to learn linear separators within generalization error ε. We then prese...