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» Machine Learning by Function Decomposition
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FTML
2008
185views more  FTML 2008»
14 years 11 months ago
Graphical Models, Exponential Families, and Variational Inference
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
Martin J. Wainwright, Michael I. Jordan
DATAMINE
2006
157views more  DATAMINE 2006»
14 years 11 months ago
Data Clustering with Partial Supervision
Clustering with partial supervision finds its application in situations where data is neither entirely nor accurately labeled. This paper discusses a semisupervised clustering algo...
Abdelhamid Bouchachia, Witold Pedrycz
IJON
2006
131views more  IJON 2006»
14 years 11 months ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...
IJON
2006
146views more  IJON 2006»
14 years 11 months ago
Feature selection and classification using flexible neural tree
The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator se...
Yuehui Chen, Ajith Abraham, Bo Yang
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IJBRA
2007
80views more  IJBRA 2007»
14 years 11 months ago
On predicting secondary structure transition
A function of a protein is dependent on its structure; therefore, predicting a protein structure from an amino acid sequence is an active area of research. Optimally predicting a ...
Raja Loganantharaj, Vivek Philip