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This paper proposes a new bit allocation algorithm, capable of efficiently allocating a given quota of hits to an arbitrary set of different quantizers. This algorithm is useful in any coding scheme which employs hit allocation or, more generally, codebook allocation. It produces an optimal or very nearly optimal allocation, while allowing the set of(More)
—We propose a new learning algorithm for regression modeling. The method is especially suitable for optimizing neural network structures that are amenable to a statistical description as mixture models. These include mixture of experts, hierarchical mixture of experts (HME), and normalized radial basis functions (NRBF). Unlike recent maximum likelihood (ML)(More)
A global optimization method is introduced for the design of statistical classiiers that minimize the rate of misclassiication. We rst derive the theoretical basis for the method, based on which we develop a novel design algorithm and demonstrate its eeectiveness and superior performance in the design of practical classiiers for some of the most popular(More)