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» Evaluating algorithms that learn from data streams
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ICCV
2005
IEEE
16 years 8 months ago
Geometric Context from a Single Image
Many computer vision algorithms limit their performance by ignoring the underlying 3D geometric structure in the image. We show that we can estimate the coarse geometric propertie...
Derek Hoiem, Alexei A. Efros, Martial Hebert
INFOCOM
2012
IEEE
13 years 9 months ago
Quality-assured cloud bandwidth auto-scaling for video-on-demand applications
—There has been a recent trend that video-on-demand (VoD) providers such as Netflix are leveraging resources from cloud services for multimedia streaming. In this paper, we cons...
Di Niu, Hong Xu, Baochun Li, Shuqiao Zhao
KDD
2004
ACM
124views Data Mining» more  KDD 2004»
15 years 12 months ago
Incorporating prior knowledge with weighted margin support vector machines
Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus ...
Xiaoyun Wu, Rohini K. Srihari
ICML
2010
IEEE
15 years 7 months ago
Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes
Most existing algorithms for clinical risk stratification rely on labeled training data. Collecting this data is challenging for clinical conditions where only a small percentage ...
Zeeshan Syed, Ilan Rubinfeld
ICPR
2002
IEEE
16 years 7 months ago
Context-Sensitive Bayesian Classifiers and Application to Mouse Pressure Pattern Classification
In this paper, we propose a new context-sensitive Bayesian learning algorithm. By modeling the distributions of data locations by a mixture of Gaussians, the new algorithm can uti...
Yuan (Alan) Qi, Rosalind W. Picard