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» Boosting Methods for Regression
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97
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NIPS
1993
15 years 5 months ago
Fast Pruning Using Principal Components
We present a new algorithm for eliminating excess parameters and improving network generalization after supervised training. The method, \Principal Components Pruning (PCP)",...
Asriel U. Levin, Todd K. Leen, John E. Moody
128
Voted
CORR
2008
Springer
80views Education» more  CORR 2008»
15 years 3 months ago
Multi-Layer Perceptrons and Symbolic Data
In some real world situations, linear models are not sufficient to represent accurately complex relations between input variables and output variables of a studied system. Multila...
Fabrice Rossi, Brieuc Conan-Guez
118
Voted
CSDA
2010
111views more  CSDA 2010»
15 years 3 months ago
Robust online signal extraction from multivariate time series
We introduce robust regression-based online filters for multivariate time series and discuss their performance in real time signal extraction settings. We focus on methods that ca...
Vivian Lanius, Ursula Gather
JMLR
2006
112views more  JMLR 2006»
15 years 3 months ago
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
We develop kernels for measuring the similarity between relational instances using background knowledge expressed in first-order logic. The method allows us to bridge the gap betw...
Andrea Passerini, Paolo Frasconi, Luc De Raedt
135
Voted
PAMI
2006
114views more  PAMI 2006»
15 years 3 months ago
Nonparametric Supervised Learning by Linear Interpolation with Maximum Entropy
Nonparametric neighborhood methods for learning entail estimation of class conditional probabilities based on relative frequencies of samples that are "near-neighbors" of...
Maya R. Gupta, Robert M. Gray, Richard A. Olshen