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MLDM
1999
Springer
14 years 2 months ago
Automatic Design of Multiple Classifier Systems by Unsupervised Learning
In the field of pattern recognition, multiple classifier systems based on the combination of the outputs of a set of different classifiers have been proposed as a method for the de...
Giorgio Giacinto, Fabio Roli
ICTAI
2010
IEEE
13 years 7 months ago
Unsupervised Greedy Learning of Finite Mixture Models
This work deals with a new technique for the estimation of the parameters and number of components in a finite mixture model. The learning procedure is performed by means of a expe...
Nicola Greggio, Alexandre Bernardino, Cecilia Lasc...
ICPR
2006
IEEE
14 years 11 months ago
Unsupervised Segmentation Using Gabor Wavelets and Statistical Features in LIDAR Data Analysis
In this paper, we address issues in segmentation of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contai...
Hong Wei, Marc Bartels
NIPS
2008
13 years 11 months ago
Learning Taxonomies by Dependence Maximization
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the ...
Matthew B. Blaschko, Arthur Gretton
SDM
2004
SIAM
218views Data Mining» more  SDM 2004»
13 years 11 months ago
Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Ashok N. Srivastava