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2000•Unpublished venueRequires access

Limitations of hybrid systems

Barbara Hammer

Open publisher page 2 citations

Abstract

Abstract. We examine the ability of combining symbolic and subsym-bolic approaches b y means of recursively encoding and decoding struc-tured data. We show that encoding of symbolic data is possible in this w ay { hence neural netw orks seem well suited for control or classication in symbolic approaches { whereas decoding requires an increasing com-plexit y of the decoding function { hence netw orks with this dynamics are not adequate for producing structured data. Real labeled tree structures reject a smooth encoding in general. 1.

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What this paper is about

Abstract. We examine the ability of combining symbolic and subsym-bolic approaches b y means of recursively encoding and decoding struc-tured data. We show that encoding of symbolic data is possible in this w ay { hence neural netw orks seem well suited for control or classication in symbolic approaches { whereas decoding requires an increasing com-plexit y of the decoding function { hence netw orks with this dynamics are not adequate for producing structured data. Real labeled tree structures reject a smooth encoding in general. 1.

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Available abstract

Abstract. We examine the ability of combining symbolic and subsym-bolic approaches b y means of recursively encoding and decoding struc-tured data. We show that encoding of symbolic data is possible in this w ay { hence neural netw orks seem well suited for control or classication in symbolic approaches { whereas decoding requires an increasing com-plexit y of the decoding function { hence netw orks with this dynamics are not adequate for producing structured data. Real labeled tree structures reject a smooth encoding in general. 1.

Key concepts: Decoding methods, Encoding (memory), Computer science, Tree (set theory), Function (biology), Theoretical computer science, Algorithm, Artificial neural network

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