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AI
1998
Springer
13 years 7 months ago
Worst-Case Analysis of the Perceptron and Exponentiated Update Algorithms
The absolute loss is the absolute difference between the desired and predicted outcome. This paper demonstrates worst-case upper bounds on the absolute loss for the Perceptron le...
Tom Bylander
AAAI
1997
13 years 8 months ago
Worst-Case Absolute Loss Bounds for Linear Learning Algorithms
The absolute loss is the absolute difference between the desired and predicted outcome. I demonstrateworst-case upper bounds on the absolute loss for the perceptron algorithm and ...
Tom Bylander
ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
14 years 2 months ago
Worst-Case and Smoothed Analysis of k-Means Clustering with Bregman Divergences
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
Bodo Manthey, Heiko Röglin
STACS
2005
Springer
14 years 29 days ago
Worst-Case and Average-Case Approximations by Simple Randomized Search Heuristics
Abstract. In recent years, probabilistic analyses of algorithms have received increasing attention. Despite results on the average-case complexity and smoothed complexity of exact ...
Carsten Witt
COLT
2005
Springer
14 years 1 months ago
A New Perspective on an Old Perceptron Algorithm
Abstract. We present a generalization of the Perceptron algorithm. The new algorithm performs a Perceptron-style update whenever the margin of an example is smaller than a predefi...
Shai Shalev-Shwartz, Yoram Singer