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» Learning from Multiple Sources of Inaccurate Data
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ICPR
2010
IEEE
13 years 12 months ago
Pattern Recognition Using Functions of Multiple Instances
The Functions of Multiple Instances (FUMI) method for learning a target prototype from data points that are functions of target and non-target prototypes is introduced. In this pa...
Alina Zare, Paul Gader
COMAD
2008
13 years 10 months ago
Information Integration Across Heterogeneous Sources: Where Do We Stand and How to Proceed?
Today, information integration has assumed a completely different, complex connotation than what it used to be. The advent of the Internet, the proliferation of information source...
Aditya Telang, Sharma Chakravarthy, Yan Huang
ICML
2005
IEEE
14 years 9 months ago
Hierarchic Bayesian models for kernel learning
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
Mark Girolami, Simon Rogers
ICML
2004
IEEE
14 years 9 months ago
A multiplicative up-propagation algorithm
We present a generalization of the nonnegative matrix factorization (NMF), where a multilayer generative network with nonnegative weights is used to approximate the observed nonne...
Jong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh
GECCO
2004
Springer
102views Optimization» more  GECCO 2004»
14 years 2 months ago
Dynamic and Scalable Evolutionary Data Mining: An Approach Based on a Self-Adaptive Multiple Expression Mechanism
Data mining has recently attracted attention as a set of efficient techniques that can discover patterns from huge data. More recent advancements in collecting massive evolving da...
Olfa Nasraoui, Carlos Rojas, Cesar Cardona