C4.5: programs for machine learning
C4.5: programs for machine learning
Predicting springback in sheet metal forming: an explicit to implicit sequential solution procedure
Finite Elements in Analysis and Design
Computer Graphics: Mathematical First Steps
Computer Graphics: Mathematical First Steps
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network Model for the Automated Control of Springback in Rebars
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Obtaining Best Parameter Values for Accurate Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Rule Extraction from Support Vector Machines
Rule Extraction from Support Vector Machines
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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A mechanism for describing 3-D local geometries is presented which is suitable for input into a classifier generator. The objective is to predict the springback that will occur when Asymmetric Incremental Sheet Forming (AISF) is applied to sheet metal to produce a desired shape so that corrective measures can be applied. The springback is localised hence the desired before shape and the actual after shape are expressed using the concept of a Local Geometry Matrix (LGMs). The reported evaluation demonstrates that the LGM idea can be usefully employed to capture local geometries with respect to individual shapes.