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DCG
2008
104views more  DCG 2008»
15 years 5 months ago
Finding the Homology of Submanifolds with High Confidence from Random Samples
Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling...
Partha Niyogi, Stephen Smale, Shmuel Weinberger
150
Voted
RAS
2006
100views more  RAS 2006»
15 years 4 months ago
Sampling and node adding in probabilistic roadmap planners
The probabilistic roadmap approach is one of the leading motion planning techniques. Over the past decade the technique has been studied by many different researchers. This has le...
Roland Geraerts, Mark H. Overmars
CSDA
2007
152views more  CSDA 2007»
15 years 4 months ago
Robust variable selection using least angle regression and elemental set sampling
In this paper we address the problem of selecting variables or features in a regression model in the presence of both additive (vertical) and leverage outliers. Since variable sel...
Lauren McCann, Roy E. Welsch
188
Voted
ISNN
2010
Springer
15 years 3 months ago
MULP: A Multi-Layer Perceptron Application to Long-Term, Out-of-Sample Time Series Prediction
Abstract. A forecasting approach based on Multi-Layer Perceptron (MLP) Artificial Neural Networks (named by the authors MULP) is proposed for the NN5 111 time series long-term, out...
Eros Pasero, Giovanni Raimondo, Suela Ruffa
ICCV
2011
IEEE
14 years 4 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille