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» Unsupervised Greedy Learning of Finite Mixture Models
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CVPR
2000
IEEE
14 years 9 months ago
Towards Automatic Discovery of Object Categories
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
Markus Weber, Max Welling, Pietro Perona
CDC
2009
IEEE
149views Control Systems» more  CDC 2009»
13 years 8 months ago
Context-dependent multi-class classification with unknown observation and class distributions with applications to bioinformatic
We consider the multi-class classification problem, based on vector observation sequences, where the conditional (given class observations) probability distributions for each class...
Alex S. Baras, John S. Baras
JMLR
2008
83views more  JMLR 2008»
13 years 7 months ago
Generalization from Observed to Unobserved Features by Clustering
We argue that when objects are characterized by many attributes, clustering them on the basis of a random subset of these attributes can capture information on the unobserved attr...
Eyal Krupka, Naftali Tishby
ICPR
2008
IEEE
14 years 9 months ago
A novel Gaussianized vector representation for natural scene categorization
This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of featur...
Hao Tang, Mark Hasegawa-Johnson, Thomas S. Huang, ...
AI
2006
Springer
13 years 7 months ago
Robot introspection through learned hidden Markov models
In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behaviour...
Maria Fox, Malik Ghallab, Guillaume Infantes, Dere...