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ICML
2004
IEEE
14 years 1 months ago
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...
Hisashi Kashima, Yuta Tsuboi
MIA
2010
170views more  MIA 2010»
13 years 2 months ago
Linear intensity-based image registration by Markov random fields and discrete optimization
We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov Random Field (MRF) formulation. Here, the challenge arises ...
Darko Zikic, Ben Glocker, Oliver Kutter, Martin Gr...
SP
1999
IEEE
194views Security Privacy» more  SP 1999»
14 years 3 days ago
Detecting Intrusions using System Calls: Alternative Data Models
Intrusion detection systems rely on a wide variety of observable data to distinguish between legitimate and illegitimate activities. In this paper we study one such observable-seq...
Christina Warrender, Stephanie Forrest, Barak A. P...
CAIP
2009
Springer
182views Image Analysis» more  CAIP 2009»
14 years 14 days ago
New Lane Model and Distance Transform for Lane Detection and Tracking
Particle filtering of boundary points is a robust way to estimate lanes. This paper introduces a new lane model in correspondence to this particle filterbased approach, which is ...
Ruyi Jiang, Reinhard Klette, Tobi Vaudrey, Shigang...
ISBI
2008
IEEE
14 years 2 months ago
Support vector driven Markov random fields towards DTI segmentation of the human skeletal muscle
In this paper we propose a classification-based method towards the segmentation of diffusion tensor images. We use Support Vector Machines to classify diffusion tensors and we ex...
Radhouène Neji, Gilles Fleury, Jean Francoi...