Large Patterns Make Great Symbols: An Example of Learning from Example

  title={Large Patterns Make Great Symbols: An Example of Learning from Example},
  author={P. Kanerva},
  booktitle={Hybrid Neural Systems},
  • P. Kanerva
  • Published in Hybrid Neural Systems 1998
  • Mathematics, Computer Science
  • We look at distributed representation of structure with variable binding, that is natural for neural nets and that allows traditional symbolic representation and processing. The representation supports learning from example. This is demonstrated by taking several instances of the mother-of relation implying the parent-of relation, by encoding them into a mapping vector, and by showing that the mapping vector maps new instances of mother-of into parent-of. Possible implications to AI are… CONTINUE READING
    28 Citations

    Figures and Topics from this paper

    Explore Further: Topics Discussed in This Paper

    Analogical mapping and inference with binary spatter codes and sparse distributed memory
    • 28
    • Highly Influenced
    • PDF
    Holistic processing of hierarchical structures in connectionist networks
    • 23
    Resonator networks for factoring distributed representations of data structures
    • 1
    • PDF
    Some approaches to analogical mapping with structure-sensitive distributed representations
    • 18
    Hybrid Neural Systems
    • 173
    • PDF
    Randomly connected sigma–pi neurons can form associator networks
    • 30


    Recursive Distributed Representations
    • 930
    • PDF
    Features of distributed representations for tree-structures: A study of RAAM
    • 9
    Syntactic Transformations on Distributed Representations
    • 268
    • PDF
    Distributed representations and nested compositional structure
    • 167
    • PDF
    Mapping Part-Whole Hierarchies into Connectionist Networks
    • 351
    • Highly Influential
    • PDF
    Hybrid Approaches to Neural Network-based Language Processing
    • 12
    Binary Spatter-Coding of Ordered K-Tuples
    • 115