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» Learning and Generalization with the Information Bottleneck
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KDD
2004
ACM
151views Data Mining» more  KDD 2004»
15 years 10 months ago
Feature selection in scientific applications
Numerous applications of data mining to scientific data involve the induction of a classification model. In many cases, the collection of data is not performed with this task in m...
Erick Cantú-Paz, Shawn Newsam, Chandrika Ka...
CVPR
2007
IEEE
15 years 4 months ago
Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association
We propose a framework for general multiple target tracking, where the input is a set of candidate regions in each frame, as obtained from a state of the art background learning, ...
Qian Yu, Gérard G. Medioni, Isaac Cohen
ISESE
2006
IEEE
15 years 4 months ago
Using observational pilot studies to test and improve lab packages
Controlled experiments are a key approach to evaluate and evolve our understanding of software engineering technologies. However, defining and running a controlled experiment is a...
Manoel G. Mendonça, Daniela Cruzes, Josemei...
ILP
2004
Springer
15 years 3 months ago
Modelling Inhibition in Metabolic Pathways Through Abduction and Induction
Abstract. In this paper, we study how a logical form of scientific modelling that integrates together abduction and induction can be used to understand the functional class of unk...
Alireza Tamaddoni-Nezhad, Antonis C. Kakas, Stephe...
FTML
2008
185views more  FTML 2008»
14 years 10 months ago
Graphical Models, Exponential Families, and Variational Inference
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
Martin J. Wainwright, Michael I. Jordan