Symbolic and Subsymbolic Learning for Vision: Some Possibilities

Contents

Robust, flexible and sufficiently general vision systems such as those for recognition and description of complex 3- dimensional objects require an adequate armamentarium of representations and learning mechanisms. This paper briefly analyzes the strengths and weaknesses of different learning paradigms such as symbol processing systems, connectionist networks, and statistical and syntactic pattern recognition systems as possible candidates for providing such capabilities and points out several promising directions for integrating multiple such paradigms in a synergistic fashion towards that goal.