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EMMCVPR
2011
Springer
12 years 8 months ago
Multiple-Instance Learning with Structured Bag Models
Traditional approaches to Multiple-Instance Learning (MIL) operate under the assumption that the instances of a bag are generated independently, and therefore typically learn an in...
Jonathan Warrell, Philip H. S. Torr
JMLR
2012
11 years 11 months ago
Sample Complexity of Composite Likelihood
We present the first PAC bounds for learning parameters of Conditional Random Fields [12] with general structures over discrete and real-valued variables. Our bounds apply to com...
Joseph K. Bradley, Carlos Guestrin
MOC
2000
132views more  MOC 2000»
13 years 8 months ago
Lattice computations for random numbers
We improve on a lattice algorithm of Tezuka for the computation of the k-distribution of a class of random number generators based on finite fields. We show how this is applied to ...
Raymond Couture, Pierre L'Ecuyer
NIPS
2003
13 years 10 months ago
Discriminative Fields for Modeling Spatial Dependencies in Natural Images
In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial depe...
Sanjiv Kumar, Martial Hebert
INFORMATICALT
2006
150views more  INFORMATICALT 2006»
13 years 8 months ago
A Multiresolution Approach Based on MRF and Bak-Sneppen Models for Image Segmentation
The two major Markov Random Fields (MRF) based algorithms for image segmentation are the Simulated Annealing (SA) and Iterated Conditional Modes (ICM). In practice, compared to the...
Kamal E. Melkemi, Mohamed Batouche, Sebti Foufou