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» Knowledge Discovery Via Multiple Models
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LION
2010
Springer
209views Optimization» more  LION 2010»
15 years 2 months ago
Feature Extraction from Optimization Data via DataModeler's Ensemble Symbolic Regression
We demonstrate a means of knowledge discovery through feature extraction that exploits the search history of an optimization run. We regress a symbolic model ensemble from optimiza...
Kalyan Veeramachaneni, Katya Vladislavleva, Una-Ma...
EMNLP
2011
13 years 9 months ago
Structured Relation Discovery using Generative Models
We explore unsupervised approaches to relation extraction between two named entities; for instance, the semantic bornIn relation between a person and location entity. Concretely, ...
Limin Yao, Aria Haghighi, Sebastian Riedel, Andrew...
MICAI
2004
Springer
15 years 2 months ago
Extracting Temporal Patterns from Time Series Data Bases for Prediction of Electrical Demand
In this paper we present a technique for prediction of electrical demand based on multiple models. The multiple models are composed by several local models, each one describing a r...
J. Jesus Rico Melgoza, Juan J. Flores, Constantino...
ICASSP
2007
IEEE
15 years 3 months ago
Evolutionary Sequence Modeling for Discovery of Peptide Hormones
There are currently a large number of ‘‘orphan’’ G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these pepti...
M. Kemal Sönmez, Lawrence Toll, Nina Zaveri
SBP
2010
Springer
15 years 4 months ago
A Knowledge Collaboration Network Model across Disciplines
Abstract. We propose a theoretical framework for the optimal collaboration among researchers in a knowledge network in which researchers are not limited to a single discipline and ...
Anna Nagurney, Qiang Qiang