An adaption mechanism for the error threshold of XCSF

Author(s):  
Tim Hansmeier ◽  
Paul Kaufmann ◽  
Marco Platzner
Keyword(s):  
Author(s):  
David Blondheim

AbstractMachine learning (ML) is unlocking patterns and insight into data to provide financial value and knowledge for organizations. Use of machine learning in manufacturing environments is increasing, yet sometimes these applications fail to produce meaningful results. A critical review of how defects are classified is needed to appropriately apply machine learning in a production foundry and other manufacturing processes. Four elements associated with defect classification are proposed: Binary Acceptance Specifications, Stochastic Formation of Defects, Secondary Process Variation, and Visual Defect Inspection. These four elements create data space overlap, which influences the bias associated with training supervised machine learning algorithms. If this influence is significant enough, the predicted error of the model exceeds a critical error threshold (CET). There is no financial motivation to implement the ML model in the manufacturing environment if its error is greater than the CET. The goal is to bring awareness to these four elements, define the critical error threshold, and offer guidance and future study recommendations on data collection and machine learning that will increase the success of ML within manufacturing.


2014 ◽  
Vol 9 ◽  
pp. 138-145
Author(s):  
N.S.S. Singh ◽  
N.H. Hamid ◽  
V.S. Asirvadam

Author(s):  
Jason C. Bartram ◽  
Dominic Thewlis ◽  
David T. Martin ◽  
Kevin I. Norton

Purpose: Modeling intermittent work capacity is an exciting development to the critical power model with many possible applications across elite sport. With the Skiba 2 model validated using subelite participants, an adjustment to the model’s recovery rate has been proposed for use in elite cyclists (Bartram adjustment). The team pursuit provides an intermittent supramaximal event with which to validate the modeling of W′ in this population. Methods: Team pursuit data of 6 elite cyclists competing for Australia at a Track World Cup were solved for end W′ values using both the Skiba 2 model and the Bartram adjustment. Each model’s success was evaluated by its ability to approximate end W′ values of 0 kJ, as well as a count of races modeled to within a predetermined error threshold of ±1.840 kJ. Results: On average, using the Skiba 2 model found end W′ values different from zero (P = .007; mean ± 95% confidence limit, –2.7 ± 2.0 kJ), with 3 out of 8 cases ending within the predetermined error threshold. Using the Bartram adjustment on average resulted in end W′ values that were not different from zero (P = .626; mean ± 95% confidence limit, 0.5 ± 2.5 kJ), with 4 out of 8 cases falling within the predetermined error threshold. Conclusions: On average, the Bartram adjustment was an improvement to modeling intermittent work capacity in elite cyclists, with the Skiba 2 model underestimating the rate of W′ recovery. In the specific context of modeling team pursuit races, all models were too variable for effective use; hence, individual recovery rates should be explored beyond population-specific rates.


2005 ◽  
Vol 107 (2) ◽  
pp. 117-127 ◽  
Author(s):  
Christof K. Biebricher ◽  
Manfred Eigen
Keyword(s):  

1988 ◽  
Vol 134 (4) ◽  
pp. 431-443 ◽  
Author(s):  
Alvaro García-Tejedor ◽  
Juan Carlos Sanz-Nuño ◽  
José Olarrea ◽  
Francisco Javier de la Rubia ◽  
Francisco Montero

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