Search Results - "Maiorov, Vitaly"

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  1. 1

    On the optimality of neural-network approximation using incremental algorithms by Meir, R., Maiorov, V.E.

    Published in IEEE transactions on neural networks (01-03-2000)
    “…The problem of approximating functions by neural networks using incremental algorithms is studied. For functions belonging to a rather general class,…”
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  2. 2

    Geometric properties of the ridge function manifold by Maiorov, Vitaly

    Published in Advances in computational mathematics (01-02-2010)
    “…We study geometrical properties of the ridge function manifold consisting of all possible linear combinations of n functions of the form g ( a · x ), where a ·…”
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  3. 3

    Lower bounds for approximation by MLP neural networks by Maiorov, Vitaly, Pinkus, Allan

    Published in Neurocomputing (Amsterdam) (01-04-1999)
    “…The degree of approximation by a single hidden layer MLP model with n units in the hidden layer is bounded below by the degree of approximation by a linear…”
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  4. 4

    On best approximation of classes by radial functions by Maiorov, Vitaly

    Published in Journal of approximation theory (2003)
    “…We investigate the radial manifolds R n generated by a linear combination of n radial functions on R d . We consider the best approximation of function classes…”
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  5. 5

    Lower bounds for multivariate approximation by affine-invariant dictionaries by Maiorov, V., Meir, R.

    Published in IEEE transactions on information theory (01-05-2001)
    “…The problem of approximating locally smooth multivariate functions by linear combinations of elements from an affine-invariant redundant dictionary is…”
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  6. 6

    On the Value of Partial Information for Learning from Examples by Ratsaby, Joel, Maiorov, Vitaly

    Published in Journal of Complexity (01-12-1997)
    “…The PAC model of learning and its extension to real valued function classes provides a well-accepted theoretical framework for representing the problem of…”
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  7. 7

    On the Learnability of Rich Function Classes by Ratsaby, Joel, Maiorov, Vitaly

    Published in Journal of computer and system sciences (01-02-1999)
    “…The probably approximately correct (PAC) model of learning and its extension to real-valued function classes sets a rigorous framework based upon which the…”
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  8. 8

    On the Approximation of Functional Classes Equipped with a Uniform Measure Using Ridge Functions by Maiorov, Vitaly, Meir, Ron, Ratsaby, Joel

    Published in Journal of approximation theory (01-07-1999)
    “…We introduce a construction of a uniform measure over a functional class Br which is similar to a Besov class with smoothness index r. We then consider the…”
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  9. 9

    The degree of approximation of sets in euclidean space using sets with bounded Vapnik-Chervonenkis dimension by Maiorov, Vitaly, Ratsaby, Joel

    Published in Discrete Applied Mathematics (18-08-1998)
    “…The degree of approximation of infinite-dimensional function classes using finite n-dimensional manifolds has been the subject of a classical field of study in…”
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  10. 10

    Error bounds for functional approximation and estimation using mixtures of experts by Zeevi, A.J., Meir, R., Maiorov, V.

    Published in IEEE transactions on information theory (01-05-1998)
    “…We examine some mathematical aspects of learning unknown mappings with the mixture of experts model (MEM). Specifically, we observe that the MEM is at least as…”
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  11. 11

    Distortion bounds for vector quantizers with finite codebook size by Meir, R., Maiorov, V.

    Published in IEEE transactions on information theory (01-07-1999)
    “…Upper and lower bounds are presented for the distortion of the optimal N-point vector quantizer applied to k-dimensional signals. Under certain smoothness…”
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  12. 12

    Approximation bounds for smooth functions in C(R/sup d/) by neural and mixture networks by Maiorov, V., Meir, R.S.

    Published in IEEE transactions on neural networks (01-09-1998)
    “…We consider the approximation of smooth multivariate functions in C(R/sup d/) by feedforward neural networks with a single hidden layer of nonlinear ridge…”
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