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» Manifold-adaptive dimension estimation
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SBBD
2000
168views Database» more  SBBD 2000»
14 years 11 months ago
Fast Feature Selection Using Fractal Dimension
Dimensionalitycurse and dimensionalityreduction are two issues that have retained highinterest for data mining, machine learning, multimedia indexing, and clustering. We present a...
Caetano Traina Jr., Agma J. M. Traina, Leejay Wu, ...
COLT
1999
Springer
15 years 2 months ago
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation
The empirical error on a test set, the hold-out estimate, often is a more reliable estimate of generalization error than the observed error on the training set, the training estim...
Avrim Blum, Adam Kalai, John Langford
ACL
2008
14 years 11 months ago
Trainable Generation of Big-Five Personality Styles through Data-Driven Parameter Estimation
Previous work on statistical language generation has primarily focused on grammaticality and naturalness, scoring generation possibilities according to a language model or user fe...
François Mairesse, Marilyn A. Walker
ICPR
2006
IEEE
15 years 11 months ago
Exploiting High Dimensional Video Features Using Layered Gaussian Mixture Models
Analysis of video data usually requires training classifiers in high dimensional feature spaces. This paper proposes a layered Gaussian mixture model (LGMM) to exploit high dimens...
Datong Chen, Jie Yang
ICML
2005
IEEE
15 years 10 months ago
Supervised dimensionality reduction using mixture models
Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We...
Sajama, Alon Orlitsky