An Event-Space Feedforward Network Using Maximum Entropy Partitioning with Application to Low Level Speech Data

Autor:	David Chiu, D. Bockus, J. Bradford
Rok vydání:	1997
Předmět:	ComputingMethodologies_PATTERNRECOGNITION Computer science business.industry Principle of maximum entropy Simulated data Feed forward Speaker identification Pattern recognition Artificial intelligence Space (mathematics) Class discrimination business Event (probability theory)
Zdroj:	Mathematics of Neural Networks ISBN: 9781461377948
DOI:	10.1007/978-1-4615-6099-9_22
Popis:	This paper describes an event-space feedforward network based on partitioning of the input space using maximum entropy criterion. It shows how primitives defined as partitioned hypercells (event space) can be selected for the purpose of class discrimination. Class discrimination of a hypercell is evaluated statistically. Observed primitives corresponding to observed characteristics in selected hypercells are used as inputs to a feedforward network in classification. Preliminary experimental results using simulated data and as it pertains to speaker discrimination using low-level speech data have shown very good classification rates.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::76921ae8c279216949191797ed15dc50 https://doi.org/10.1007/978-1-4615-6099-9_22 Zobrazit plný text záznamu