Layered Logic Classifiers: Exploring the `And' and `Or' Relations
This addresses the need for more efficient and generalizable classifiers in machine learning and computer vision, though it appears incremental as it builds on existing logic-based methods.
The paper tackled the problem of designing classifiers that effectively handle complex patterns like 'xor' by proposing layered logic classifiers that combine 'and', 'or', and 'not' operations. The result showed significant improvements over AdaBoost on multiple datasets, with reduced training complexity.
Designing effective and efficient classifier for pattern analysis is a key problem in machine learning and computer vision. Many the solutions to the problem require to perform logic operations such as `and', `or', and `not'. Classification and regression tree (CART) include these operations explicitly. Other methods such as neural networks, SVM, and boosting learn/compute a weighted sum on features (weak classifiers), which weakly perform the 'and' and 'or' operations. However, it is hard for these classifiers to deal with the 'xor' pattern directly. In this paper, we propose layered logic classifiers for patterns of complicated distributions by combining the `and', `or', and `not' operations. The proposed algorithm is very general and easy to implement. We test the classifiers on several typical datasets from the Irvine repository and two challenging vision applications, object segmentation and pedestrian detection. We observe significant improvements on all the datasets over the widely used decision stump based AdaBoost algorithm. The resulting classifiers have much less training complexity than decision tree based AdaBoost, and can be applied in a wide range of domains.