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» Learning from Multiple Sources of Inaccurate Data
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AAAI
2008
15 years 2 months ago
Manifold Integration with Markov Random Walks
Most manifold learning methods consider only one similarity matrix to induce a low-dimensional manifold embedded in data space. In practice, however, we often use multiple sensors...
Heeyoul Choi, Seungjin Choi, Yoonsuck Choe
IJPRAI
2002
93views more  IJPRAI 2002»
14 years 11 months ago
Improving Stability of Decision Trees
Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. B...
Mark Last, Oded Maimon, Einat Minkov
IBMRD
2011
112views more  IBMRD 2011»
14 years 6 months ago
Sensor Andrew: Large-scale campus-wide sensing and actuation
—We present Sensor Andrew, a multi-disciplinary campus-wide scalable sensor network that is designed to host a wide range of sensor, actuator and low-power applications. The goal...
Anthony Rowe, Mario Berges, Gaurav Bhatia, Ethan G...
BMCBI
2007
172views more  BMCBI 2007»
14 years 12 months ago
msBayes: Pipeline for testing comparative phylogeographic histories using hierarchical approximate Bayesian computation
Background: Although testing for simultaneous divergence (vicariance) across different population-pairs that span the same barrier to gene flow is of central importance to evoluti...
Michael J. Hickerson, Eli Stahl, Naoki Takebayashi
CORR
2010
Springer
103views Education» more  CORR 2010»
14 years 10 months ago
Structural Solutions to Dynamic Scheduling for Multimedia Transmission in Unknown Wireless Environments
In this paper, we propose a systematic solution to the problem of scheduling delay-sensitive media data for transmission over time-varying wireless channels. We first formulate th...
Fangwen Fu, Mihaela van der Schaar