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» Accurate max-margin training for structured output spaces
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JMLR
2012
11 years 10 months ago
Perturbation based Large Margin Approach for Ranking
We consider the task of devising large-margin based surrogate losses for the learning to rank problem. In this learning to rank setting, the traditional hinge loss for structured ...
Eunho Yang, Ambuj Tewari, Pradeep D. Ravikumar
ICIP
2007
IEEE
14 years 9 months ago
3D Human Motion Tracking using Manifold Learning
This paper introduces a framework to track 3D human movement using Gaussian process dynamic model (GPDM) and particle filter. The framework combines the particle filter and discri...
Feng Guo, Gang Qian
AIPS
2008
13 years 10 months ago
What Makes Planners Predictable?
In recent work we showed that models constructed from planner performance data over a large suite of benchmark problems are surprisingly accurate; 91-99% accuracy for success and ...
Mark Roberts, Adele E. Howe, Brandon Wilson, Marie...
PREMI
2005
Springer
14 years 1 months ago
Geometric Decision Rules for Instance-Based Learning Problems
In the typical nonparametric approach to classification in instance-based learning and data mining, random data (the training set of patterns) are collected and used to design a d...
Binay K. Bhattacharya, Kaustav Mukherjee, Godfried...
WEBI
2007
Springer
14 years 1 months ago
An unsupervised hierarchical approach to document categorization
— We propose a hierarchical approach to document categorization that requires no pre-configuration and maps the semantic document space to a predefined taxonomy. The utilizatio...
Robert Wetzker, Tansu Alpcan, Christian Bauckhage,...