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AROBOTS
2011
15 years 27 days ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox
CVPR
2011
IEEE
14 years 9 months ago
Learning People Detection Models from Few Training Samples
People detection is an important task for a wide range of applications in computer vision. State-of-the-art methods learn appearance based models requiring tedious collection and ...
Leonid Pishchulin, Christian Wojek, Arjun Jain, Th...
ICCV
2011
IEEE
14 years 5 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
SDM
2007
SIAM
187views Data Mining» more  SDM 2007»
15 years 7 months ago
Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning
Topic modeling techniques have widespread use in text data mining applications. Some applications use batch models, which perform clustering on the document collection in aggregat...
Arindam Banerjee, Sugato Basu
MICCAI
2004
Springer
16 years 6 months ago
Learning Coupled Prior Shape and Appearance Models for Segmentation
We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape an...
Xiaolei Huang, Zhiguo Li, Dimitris N. Metaxas