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» Adapting the Fitness Function in GP for Data Mining
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ACCV
2009
Springer
13 years 8 months ago
Adaptive-Scale Robust Estimator Using Distribution Model Fitting
We propose a new robust estimator for parameter estimation in highly noisy data with multiple structures and without prior information on the noise scale of inliers. This is a diag...
Trung Ngo Thanh, Hajime Nagahara, Ryusuke Sagawa, ...
FLAIRS
2006
13 years 9 months ago
Improving Modularity in Genetic Programming Using Graph-Based Data Mining
We propose to improve the efficiency of genetic programming, a method to automatically evolve computer programs. We use graph-based data mining to identify common aspects of highl...
Istvan Jonyer, Akiko Himes
SAC
2008
ACM
13 years 7 months ago
Local linear regression with adaptive orthogonal fitting for the wind power application
For short-term forecasting of wind generation, a necessary step is to model the function for the conversion of meteorological variables (mainly wind speed) to power production. Su...
Pierre Pinson, Henrik Aalborg Nielsen, Henrik Mads...
KDD
2004
ACM
179views Data Mining» more  KDD 2004»
14 years 8 months ago
1-dimensional splines as building blocks for improving accuracy of risk outcomes models
Transformation of both the response variable and the predictors is commonly used in fitting regression models. However, these transformation methods do not always provide the maxi...
David S. Vogel, Morgan C. Wang
CORR
2006
Springer
153views Education» more  CORR 2006»
13 years 7 months ago
Genetic Programming, Validation Sets, and Parsimony Pressure
Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited n...
Christian Gagné, Marc Schoenauer, Marc Pari...