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» Geometric Bounds for Generalization in Boosting
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SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 9 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
ICRA
2005
IEEE
122views Robotics» more  ICRA 2005»
14 years 29 days ago
Supervised Learning of Places from Range Data using AdaBoost
— This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of pla...
Óscar Martínez Mozos, Cyrill Stachni...
DMTCS
2010
157views Mathematics» more  DMTCS 2010»
13 years 4 months ago
Edge-Removal and Non-Crossing Configurations in Geometric Graphs
A geometric graph is a graph G = (V, E) drawn in the plane, such that V is a point set in general position and E is a set of straight-line segments whose endpoints belong to V . W...
Oswin Aichholzer, Sergio Cabello, Ruy Fabila Monro...
COMBINATORICS
2004
76views more  COMBINATORICS 2004»
13 years 7 months ago
Geometrically Constructed Bases for Homology of Partition Lattices
Abstract. We use the theory of hyperplane arrangements to construct natural bases for the homology of partition lattices of types A, B and D. This extends and explains the "sp...
Anders Björner, Michelle L. Wachs
COMGEO
2004
ACM
13 years 7 months ago
Speeding up the incremental construction of the union of geometric objects in practice
We present a new incremental algorithm for constructing the union of n triangles in the plane. In our experiments, the new algorithm, which we call the Disjoint-Cover (DC) algorit...
Eti Ezra, Dan Halperin, Micha Sharir