The nature of statistical learning theory
The nature of statistical learning theory
OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems
OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Information Sciences: an International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression
Computers and Structures
Fault tolerance in the framework of support vector machines based model predictive control
Engineering Applications of Artificial Intelligence
A novel intrusion detection system based on hierarchical clustering and support vector machines
Expert Systems with Applications: An International Journal
Pricing currency options with support vector regression and stochastic volatility model with jumps
Expert Systems with Applications: An International Journal
Structural and Multidisciplinary Optimization
Hi-index | 12.05 |
Elementary and junior high school buildings in Taiwan are designed to serve not only as places of education but also as temporary shelters in the aftermath of major earthquakes. Effective evaluation of the seismic resistance of school buildings is a critical issue that deserves further investigation. The National Center for Research on Earthquake Engineering (in Taiwan) currently employs performance-target ground acceleration (A"P) as the index to evaluate school structure compliance with seismic resistance requirements. However, computational processes are complicated, time consuming, and require the input of many experts. To address this problem, this paper developed an evolutionary support vector machine inference system (ESIS) that integrated two AI techniques, namely, the support vector machine (SVM) and fast messy genetic algorithm (fmGA). Based on training results, the developed system can predict the A"P of a school building in a significantly shorter time base, thus increasing evaluation efficiency significantly. The validity of ESIS was tested using the 10-Fold Cross-Validation method. Another aim of this paper is to retain and apply expert knowledge and relevant experience to the solution of similar problems in the future.