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» Dynamically Adapting Kernels in Support Vector Machines
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139
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COLT
2008
Springer
15 years 5 months ago
Adapting to a Changing Environment: the Brownian Restless Bandits
In the multi-armed bandit (MAB) problem there are k distributions associated with the rewards of playing each of k strategies (slot machine arms). The reward distributions are ini...
Aleksandrs Slivkins, Eli Upfal
ICPR
2008
IEEE
15 years 10 months ago
Adaptive nonstationary regression analysis
The problem of finding the most appropriate subset of features or regressors is the generic challenge of Machine Learning problems like regression estimation or pattern recognitio...
Olga Krasotkina, Vadim Mottl
158
Voted
CVPR
2010
IEEE
15 years 3 months ago
Robust RVM regression using sparse outlier model
Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision p...
Kaushik Mitra, Ashok Veeraraghavan, Rama Chellappa
145
Voted
SC
1990
ACM
15 years 7 months ago
Tracing application program execution on the Cray X-MP and Cray 2
Important insights into program operation can be gained by observing dynamic execution behavior. Unfortunately, many high-performance machines provide execution profile summaries ...
Allen D. Malony, John L. Larson, Daniel A. Reed
JMLR
2012
13 years 6 months ago
Maximum Margin Temporal Clustering
Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on e...
Minh Hoai Nguyen, Fernando De la Torre