Applications of machine learning and rule induction
Communications of the ACM
Knowledge Acquisition from Databases
Knowledge Acquisition from Databases
R-MINI: An Iterative Approach for Generating Minimal Rules from Examples
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
The catchment feature model: a device for multimodal fusion and a bridge between signal and sense
EURASIP Journal on Applied Signal Processing
Real-time hand gesture recognition using complex-valued neural network (CVNN)
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds
International Journal of Computer Vision
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This paper presents a recursive inductive learning scheme that is able to acquire hand pose models in the form of disjunctive normal form expressions involving multivalued features. Based on an extended variable-valued logic, our rule-based induction system is able to abstract compact rule sets from any set of feature vectors describing a set of classifications. The rule bases which satisfy the completeness and consistency conditions are induced and refined through five heuristic strategies. A recursive induction learning scheme in the RIEVL algorithm is designed to escape local minima in the solution space. A performance comparison of RIEVL with other inductive algorithms, ID3, NewID, C4.5, CN2, and HCV, is given in the paper. In the experiments with hand gestures, the system produced the disjunctive normal form descriptions of each pose and identified the different hand poses based on the classification rules obtained by the RIEVL algorithm. RIEVL classified 94.4 percent of the gesture images in our testing set correctly, outperforming all other inductive algorithms.