Communications of the ACM
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Learnable and Nonlearnable Visual Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Networks and the Best Approximation Property
Networks and the Best Approximation Property
Bringing the Grandmother Back into the Picture: A Memory-Based View of Object Recognition
Bringing the Grandmother Back into the Picture: A Memory-Based View of Object Recognition
Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes
International Journal of Computer Vision
Learning to recognize three-dimensional objects
Neural Computation
| Hi-index | 0.14 |
Previous results on nonlearnability of visual concepts relied on the assumption that such concepts are represented as sets of pixels. The author uses an approach developed by Haussler (1989) to show that under an alternative, feature-based representation, recognition is probably approximately correct (PAC) learnable from a feasible number of examples in a distribution-free manner.