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119
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NIPS
2007
15 years 4 months ago
Learning Visual Attributes
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ...
Vittorio Ferrari, Andrew Zisserman
IJCAI
2003
15 years 4 months ago
When Discriminative Learning of Bayesian Network Parameters Is Easy
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Hannes Wettig, Peter Grünwald, Teemu Roos, Pe...
109
Voted
JCP
2006
118views more  JCP 2006»
15 years 2 months ago
Learning a Classification-based Glioma Growth Model Using MRI Data
Gliomas are malignant brain tumors that grow by invading adjacent tissue. We propose and evaluate a 3D classification-based growth model, CDM, that predicts how a glioma will grow ...
Marianne Morris, Russell Greiner, Jörg Sander...
157
Voted
JMLR
2006
186views more  JMLR 2006»
15 years 2 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
119
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CORR
2002
Springer
99views Education» more  CORR 2002»
15 years 2 months ago
The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning
A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature ...
Peter D. Turney