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KDD
2004
ACM
135views Data Mining» more  KDD 2004»
16 years 7 months ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that driv...
Giles Hooker
KDD
2009
ACM
192views Data Mining» more  KDD 2009»
16 years 1 months ago
Primal sparse Max-margin Markov networks
Max-margin Markov networks (M3 N) have shown great promise in structured prediction and relational learning. Due to the KKT conditions, the M3 N enjoys dual sparsity. However, the...
Jun Zhu, Eric P. Xing, Bo Zhang
CIVR
2007
Springer
180views Image Analysis» more  CIVR 2007»
16 years 20 days ago
Probabilistic matching and resemblance evaluation of shapes in trademark images
We present a novel matching and similarity evaluation method for planar geometric shapes represented by sets of polygonal curves. Given two shapes, the matching algorithm randomly...
Helmut Alt, Ludmila Scharf, Sven Scholz
ARCS
2005
Springer
16 years 1 days ago
Adaptive Object Acquisition
We propose an active vision system for object acquisition. The core of our approach is a reinforcement learning module which learns a strategy to scan an object. The agent moves a...
Gabriele Peters, Claus-Peter Alberts, Markus Bries...
GECCO
2005
Springer
186views Optimization» more  GECCO 2005»
16 years 1 days ago
Subproblem optimization by gene correlation with singular value decomposition
Several ways of using singular value decomposition (SVD), a linear algebra technique typically used for information retrieval, to decompose problems into subproblems are investiga...
Jacob G. Martin