Concurrent grammar inference machines for 2-D pattern recognition: a comparison with the level set approach
2009
Parallel processing promises scalable and effective computing power which can handle the complex data structures of
knowledge representation languages efficiently. Past and present sequential architectures, despite the rapid advances in
computing technology, have yet to provide such processing power and to offer a holistic solution to the problem. This
paper presents a fresh attempt in formulating alternative techniques for grammar learning, based upon the parallel and
distributed model of connectionism , to facilitate the more cognitively demanding task of pattern understanding. The
proposed method has been compared with the contemporary approach of shape modelling based on level sets, and
demonstrated its potential as a prototype for constructing robust networks on high performance parallel platforms.
Keywords:
- Data structure
- Connectionism
- Knowledge representation and reasoning
- Syntactic pattern recognition
- Inference
- Artificial intelligence
- Machine learning
- Rule-based machine translation
- Complex data type
- Computer science
- Scalability
- Inference engine
- Knowledge base
- Intelligent decision support system
- Grammar induction
- Pattern recognition
- Commonsense knowledge
- Correction
- Source
- Cite
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