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» Dynamic Modeling in Inductive Inference
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CVPR
2008
IEEE
14 years 10 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
HYBRID
2004
Springer
14 years 1 months ago
Inference Methods for Autonomous Stochastic Linear Hybrid Systems
We present a parameter inference algorithm for autonomous stochastic linear hybrid systems, which computes a maximum-likelihood model, given only a set of continuous output data of...
Hamsa Balakrishnan, Inseok Hwang, Jung Soon Jang, ...
ICIP
2003
IEEE
14 years 9 months ago
Transductive inference for color-based particle filter tracking
Robust real-time tracking of non-rigid objects in a dynamic environment is a challenging task. Among various cues in tracking, color can provide an efficient visual cue for this t...
Jiang Li, Chin-Seng Chua
ECCV
2002
Springer
14 years 9 months ago
Dynamic Trees: Learning to Model Outdoor Scenes
Abstract. This paper considers the dynamic tree (DT) model, first introduced in [1]. A dynamic tree specifies a prior over structures of trees, each of which is a forest of one or ...
Nicholas J. Adams, Christopher K. I. Williams
ICML
2004
IEEE
14 years 8 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...