Symbolic and Subsymbolic Learning for Vision: Some Possibilities
نویسنده
چکیده
Robust, flexible and sufficiently general vision systems such as those for recognition and description of complex 3dimensional 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.
منابع مشابه
Hybrid Object Models: Combining Symbolic and Subsymbolic Object Recognition Strategies
In this paper, we describe a hybrid object recognition system. The integration of biologically motivated subsymbolic image recognition and of symbolic reasoning and control mechanisms shows a rapid increase in performance, well above of each single module in the system. Starting with an holistic subsymbolic recognition system we added an AI-based symbolic recognition layer on top of our previou...
متن کاملLearning in an active hybrid vision system
This paper focuses on learning of object models for an active robot vision system. One of its main attributes is the generation of hybrid models of 3D objects, integrating implicit representations by neural networks and explicit descriptions by semantic networks. On both levels of the vision system, subsymbolic neural learning as well as symbolic semantic learning can be done completely unsuper...
متن کاملPerspectives of Neuro–Symbolic Integration
There is an obvious tension between symbolic and subsymbolic theories, because both show complementary strengths and weaknesses in corresponding applications and underlying methodologies. The resulting gap in the foundations and the applicability of these approaches is theoretically unsatisfactory and practically undesirable. We sketch a theory that bridges this gap between symbolic and subsymb...
متن کاملA Novel Strategy for Hybridizing Subsymbolic and Symbolic Learning and Representation
One approach to bridging the historic divide between ”symbolic” and ”subsymbolic” AI is to incorporate a subsymbolic system and a symbolic system into a synergetic integrative cognitive architecture. Here we consider various issues related to incorporating (subsymbolic) compositional spatiotemporal deep learning networks (CSDLNs, a term introduced to denote the category including HTM, DeSTIN an...
متن کاملof the Nonconscious Acquisition A Symbolic Model
This article presents counter evidence against Smolensky’s theory thot human intuitive/nonconscious congnitive processes can only be accurately explained in terms of subsymbolic computations carried out in artificial neural networks. We present symbolic learning models of two well-studied, complicated cognitive tasks involving nonconscious acquisition of information: learning production rules a...
متن کامل