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» On Combining Classifiers
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ICML
2007
IEEE
14 years 11 months ago
Learning to combine distances for complex representations
The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Bo...
Adam Woznica, Alexandros Kalousis, Melanie Hilario
MCS
2005
Springer
14 years 3 months ago
Mixture of Gaussian Processes for Combining Multiple Modalities
This paper describes a unified approach, based on Gaussian Processes, for achieving sensor fusion under the problematic conditions of missing channels and noisy labels. Under the ...
Ashish Kapoor, Hyungil Ahn, Rosalind W. Picard
ICRA
2002
IEEE
130views Robotics» more  ICRA 2002»
14 years 3 months ago
Combining Laser Range, Color, and Texture Cues for Autonomous Road Following
We describe results on combining depth information from a laser range-finder and color and texture image cues to segment ill-structured dirt, gravel, and asphalt roads as input t...
Christopher Rasmussen
LREC
2010
169views Education» more  LREC 2010»
13 years 11 months ago
Identification of the Question Focus: Combining Syntactic Analysis and Ontology-based Lookup through the User Interaction
Most question-answering systems contain a classifier module which determines a question category, based on which each question is assigned an answer type. However, setting up synt...
Danica Damljanovic, Milan Agatonovic, Hamish Cunni...
CAIP
2001
Springer
293views Image Analysis» more  CAIP 2001»
14 years 2 months ago
A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features
In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian est...
Zoltan Kato, Ting-Chuen Pong