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» Improving Random Forests
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106
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ICML
2010
IEEE
15 years 1 months ago
Deep networks for robust visual recognition
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common vari...
Yichuan Tang, Chris Eliasmith
114
Voted
ICASSP
2010
IEEE
15 years 1 months ago
Training a support vector machine to classify signals in a real environment given clean training data
When building a classifier from clean training data for a particular test environment, knowledge about the environmental noise and channel should be taken into account. We propos...
Kevin Jamieson, Maya R. Gupta, Eric Swanson, Hyrum...
ACTAC
2007
69views more  ACTAC 2007»
15 years 29 days ago
Synthesising Robust Schedules for Minimum Disruption Repair Using Linear Programming
An o-line scheduling algorithm considers resource, precedence, and synchronisation requirements of a task graph, and generates a schedule guaranteeing its timing requirements. Th...
Dávid Hanák, Nagarajan Kandasamy
AMAI
2008
Springer
15 years 29 days ago
Bayesian learning of Bayesian networks with informative priors
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayesian nets (BNs). Prior distributions are defined using stochastic logic programs...
Nicos Angelopoulos, James Cussens
99
Voted
CORR
2008
Springer
129views Education» more  CORR 2008»
15 years 28 days ago
Factoring Polynomials over Finite Fields using Balance Test
We study the problem of factoring univariate polynomials over finite fields. Under the assumption of the Extended Riemann Hypothesis (ERH), Gao [Gao01] designed a polynomial time ...
Chandan Saha