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SDM
2012
SIAM
237views Data Mining» more  SDM 2012»
13 years 3 months ago
A Distributed Kernel Summation Framework for General-Dimension Machine Learning
Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant technique...
Dongryeol Lee, Richard W. Vuduc, Alexander G. Gray
90
Voted
ICCV
2007
IEEE
16 years 2 months ago
Boosting Invariance and Efficiency in Supervised Learning
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
Andrea Vedaldi, Paolo Favaro, Enrico Grisan
114
Voted
ICIP
2005
IEEE
16 years 2 months ago
Visual tracking via efficient kernel discriminant subspace learning
Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning...
Chunhua Shen, Anton van den Hengel, Michael J. Bro...
ICRA
2008
IEEE
265views Robotics» more  ICRA 2008»
15 years 7 months ago
Trajectory generation for dynamic bipedal walking through qualitative model based manifold learning
— Legged robots represent great promise for transport in unstructured environments. However, it has been difficult to devise motion planning strategies that achieve a combinatio...
Subramanian Ramamoorthy, Benjamin Kuipers
101
Voted
SEKE
2004
Springer
15 years 6 months ago
Supporting the Requirements Prioritization Process. A Machine Learning approach
Requirements prioritization plays a key role in the requirements engineering process, in particular with respect to critical tasks such as requirements negotiation and software re...
Paolo Avesani, Cinzia Bazzanella, Anna Perini, Ang...