Recognizing yield patterns through hybrid applications of machine learning techniques

Machine learning in manufacturing: advantages, challenges, and applications

“…Adding to this already existing complexity, combinations of different algorithms, so-called 'hybrid approaches, ' are becoming more and more common promising better results than 'individual' single algorithm application (e.g. Lee & Ha, 2009). Many studies are available highlighting a successful application of ML techniques for specific problems.…”

“…semiconductor manufacturing) and diverse problems (e.g. process control) (Harding et al, 2006;Lee & Ha, 2009;Wang, Chen, & Lin, 2005) which highlights their main advantage: their wide applicability (Pham & Afify, 2005). Besides the wide applicability, NN are capable of handling high-dimensional and multi-variate data on a similar rate to the later introduced SVM (Kotsiantis, 2007).…”

Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

“…the optimal number of hidden nodes and for selecting the number of epochs. Lee and Ha (2009) utilized Akaike Information Criterion (AIC) to determine the optimal topology of BPNN. AIC, which is as famous as Schwartz Bayesian Criterion, picks up the optimal number of hidden nodes through a heuristic search (Burnham and Anderson, 2002).…”

Integration of similarity measurement and dynamic SVM for electrically evoked potentials prediction in visual prostheses research

“…So far, SM has been applied in a wide variety of predictive fields, e.g. finance (Chiu, 2002;Li & Ho, 2009;Oh & Kim, 2007;Shin & Han, 1999), manufacturing (Lee & Ha, 2009), agriculture Shih, Huang, Chiu, Chiud, & Hu, 2009), software development (Yang & Hsu, 2009) and medicine (Ahn & Kim, 2008;Elter, Wendtland, & Wittenberg, 2007). Nevertheless, SM belongs to the category of point-to-point computation algorithm, thus it is helpless in non-stationary time series prediction where the distribution of time series is changing over time.…”