Adaptive signal processing: theory and applications
Adaptive signal processing: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
A note on a method for generating points uniformly on n-dimensional spheres
Communications of the ACM
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Combining Decision Trees and Neural Networks for Drug Discovery
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Recursive Update Algorithm for Least Squares Support Vector Machines
Neural Processing Letters
Support Vector Machine for Regression and Applications to Financial Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
A tutorial on support vector regression
Statistics and Computing
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Applying rough sets to market timing decisions
Decision Support Systems - Special issue: Data mining for financial decision making
Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms
Management Science
A hybrid model for exchange rate prediction
Decision Support Systems
A process model to develop an internal rating system: sovereign credit ratings
Decision Support Systems
Machine assessment of neonatal facial expressions of acute pain
Decision Support Systems
A stack-based prospective spatio-temporal data analysis approach
Decision Support Systems
Using domain-specific knowledge in generalization error bounds for support vector machine learning
Decision Support Systems
Toward Automated Intelligent Manufacturing Systems (AIMS)
INFORMS Journal on Computing
Decision Support Systems
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We develop an adaptive Automated Intelligent Manufacturing System (AIMS) for Case 1:to a well-understood-pharmaceutical-process to demonstrate our methodology, Case 2:with clustering, to a not-well-controlled or understood-process for seemingly identical experiments yielding disparate results, Case 3:to scale-up a process from development to manufacturing, and Case 4:to deploy AIMS adaptively, to modify the process model and reoptimize the system contemporaneously, when predictive errors are significant. The results showed AIMS had both explanatory and predictive power. We have developed the following methodological extensions: a random probe method for feature selection, a simulation approach to establish tolerances for target inputs, and an adaptive capability integrated with statistical-process-control to modify the model.