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JMLR
2006
96views more  JMLR 2006»
9 years 11 months ago
Rearrangement Clustering: Pitfalls, Remedies, and Applications
Given a matrix of values in which the rows correspond to objects and the columns correspond to features of the objects, rearrangement clustering is the problem of rearranging the ...
Sharlee Climer, Weixiong Zhang
JMLR
2006
113views more  JMLR 2006»
9 years 11 months ago
On Inferring Application Protocol Behaviors in Encrypted Network Traffic
Several fundamental security mechanisms for restricting access to network resources rely on the ability of a reference monitor to inspect the contents of traffic as it traverses t...
Charles V. Wright, Fabian Monrose, Gerald M. Masso...
JMLR
2006
97views more  JMLR 2006»
9 years 11 months ago
Learning Coordinate Covariances via Gradients
We introduce an algorithm that learns gradients from samples in the supervised learning framework. An error analysis is given for the convergence of the gradient estimated by the ...
Sayan Mukherjee, Ding-Xuan Zhou
JMLR
2006
134views more  JMLR 2006»
9 years 11 months ago
Considering Cost Asymmetry in Learning Classifiers
Receiver Operating Characteristic (ROC) curves are a standard way to display the performance of a set of binary classifiers for all feasible ratios of the costs associated with fa...
Francis R. Bach, David Heckerman, Eric Horvitz
JMLR
2006
190views more  JMLR 2006»
9 years 11 months ago
Causal Graph Based Decomposition of Factored MDPs
We present Variable Influence Structure Analysis, or VISA, an algorithm that performs hierarchical decomposition of factored Markov decision processes. VISA uses a dynamic Bayesia...
Anders Jonsson, Andrew G. Barto
JMLR
2006
61views more  JMLR 2006»
9 years 11 months ago
In Search of Non-Gaussian Components of a High-Dimensional Distribution
Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiya...
JMLR
2006
79views more  JMLR 2006»
9 years 11 months ago
Estimation of Gradients and Coordinate Covariation in Classification
We introduce an algorithm that simultaneously estimates a classification function as well as its gradient in the supervised learning framework. The motivation for the algorithm is...
Sayan Mukherjee, Qiang Wu
JMLR
2006
186views more  JMLR 2006»
9 years 11 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
JMLR
2006
137views more  JMLR 2006»
9 years 11 months ago
Bounds for Linear Multi-Task Learning
Abstract. We give dimension-free and data-dependent bounds for linear multi-task learning where a common linear operator is chosen to preprocess data for a vector of task speci...c...
Andreas Maurer
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