Sciweavers

ICML
2008
IEEE
15 years 10 days ago
Modified MMI/MPE: a direct evaluation of the margin in speech recognition
In this paper we show how common speech recognition training criteria such as the Minimum Phone Error criterion or the Maximum Mutual Information criterion can be extended to inco...
Georg Heigold, Hermann Ney, Ralf Schlüter, Th...
ICML
2008
IEEE
15 years 10 days ago
The skew spectrum of graphs
The central issue in representing graphstructured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We pr...
Risi Imre Kondor, Karsten M. Borgwardt
ICML
2008
IEEE
15 years 10 days ago
ICA and ISA using Schweizer-Wolff measure of dependence
We propose a new algorithm for independent component and independent subspace analysis problems. This algorithm uses a contrast based on the Schweizer-Wolff measure of pairwise de...
Barnabás Póczos, Sergey Kirshner
ICML
2008
IEEE
15 years 10 days ago
An empirical evaluation of supervised learning in high dimensions
In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and stud...
Rich Caruana, Nikolaos Karampatziakis, Ainur Yesse...
ICML
2008
IEEE
15 years 10 days ago
Modeling interleaved hidden processes
Hidden Markov models assume that observations in time series data stem from some hidden process that can be compactly represented as a Markov chain. We generalize this model by as...
Niels Landwehr
ICML
2008
IEEE
15 years 10 days ago
Compressed sensing and Bayesian experimental design
Matthias W. Seeger, Hannes Nickisch
ICML
2008
IEEE
15 years 10 days ago
Random classification noise defeats all convex potential boosters
A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set. This class...
Philip M. Long, Rocco A. Servedio
ICML
2008
IEEE
15 years 10 days ago
Composite kernel learning
The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Mul...
Marie Szafranski, Yves Grandvalet, Alain Rakotomam...
ICML
2008
IEEE
15 years 10 days ago
On the hardness of finding symmetries in Markov decision processes
Shravan Matthur Narayanamurthy, Balaraman Ravindra...