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» Selection of Generative Models in Classification
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ICCV
2005
IEEE
15 years 11 months ago
Combining Generative Models and Fisher Kernels for Object Recognition
Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative approaches to learning and classification have, in...
Alex Holub, Max Welling, Pietro Perona
74
Voted
CSDA
2006
96views more  CSDA 2006»
14 years 9 months ago
Analysis of new variable selection methods for discriminant analysis
Several methods to select variables that are subsequently used in discriminant analysis are proposed and analysed. The aim is to find from among a set of m variables a smaller sub...
Joaquín A. Pacheco, Silvia Casado, Laura N&...
CIDM
2007
IEEE
15 years 4 months ago
A Prototype-driven Framework for Change Detection in Data Stream Classification
This paper presents a prototype-driven framework for classifying evolving data streams. Our framework uses cluster prototypes to summarize the data and to determine whether the cur...
Hamed Valizadegan, Pang-Ning Tan
CN
1999
148views more  CN 1999»
14 years 9 months ago
Automatic RDF Metadata Generation for Resource Discovery
Automatic metadata generation may provide a solution to the problem of inconsistent, unreliable metadata describing resources on the Web. The Resource Description Framework (RDF [...
Charlotte Jenkins, Mike Jackson, Peter Burden, Jon...
PAKDD
2011
ACM
473views Data Mining» more  PAKDD 2011»
14 years 3 months ago
 Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning
Discovering rare categories and classifying new instances of them is an important data mining issue in many fields, but fully supervised learning of a rare class classifier is pr...
Timothy Hospedales, Shaogang Gong and Tao Xiang