ACNP - Italian Periodicals Catalogue

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Results: 1-16 |

Results: 16

STRONG MINIMAX LOWER BOUNDS FOR LEARNING

Authors: ANTOS A LUGOSI G

Citation: A. Antos et G. Lugosi, STRONG MINIMAX LOWER BOUNDS FOR LEARNING, Machine learning, 30(1), 1998, pp. 31-56

LEARNING-PATTERN CLASSIFICATION - A SURVEY

Authors: KULKARNI SR LUGOSI G VENKATESH SS

Citation: Sr. Kulkarni et al., LEARNING-PATTERN CLASSIFICATION - A SURVEY, IEEE transactions on information theory, 44(6), 1998, pp. 2178-2206

THE MINIMAX DISTORTION REDUNDANCY IN EMPIRICAL QUANTIZER DESIGN

Authors: BARTLETT PL LINDER T LUGOSI G

Citation: Pl. Bartlett et al., THE MINIMAX DISTORTION REDUNDANCY IN EMPIRICAL QUANTIZER DESIGN, IEEE transactions on information theory, 44(5), 1998, pp. 1802-1813

EMPIRICAL QUANTIZER DESIGN IN THE PRESENCE OF SOURCE NOISE OR CHANNELNOISE

Authors: LINDER T LUGOSI G ZEGER K

Citation: T. Linder et al., EMPIRICAL QUANTIZER DESIGN IN THE PRESENCE OF SOURCE NOISE OR CHANNELNOISE, IEEE transactions on information theory, 43(2), 1997, pp. 612-623

NONPARAMETRIC-ESTIMATION AND CLASSIFICATION USING RADIAL BASIS FUNCTION NETS AND EMPIRICAL RISK MINIMIZATION

Authors: KRZYZAK A LINDER T LUGOSI G

Citation: A. Krzyzak et al., NONPARAMETRIC-ESTIMATION AND CLASSIFICATION USING RADIAL BASIS FUNCTION NETS AND EMPIRICAL RISK MINIMIZATION, IEEE transactions on neural networks, 7(2), 1996, pp. 475-487

A UNIVERSALLY ACCEPTABLE SMOOTHING FACTOR FOR KERNEL DENSITY ESTIMATES

Authors: DEVROYE L LUGOSI G

Citation: L. Devroye et G. Lugosi, A UNIVERSALLY ACCEPTABLE SMOOTHING FACTOR FOR KERNEL DENSITY ESTIMATES, Annals of statistics, 24(6), 1996, pp. 2499-2512

CONSISTENCY OF DATA-DRIVEN HISTOGRAM METHODS FOR DENSITY-ESTIMATION AND CLASSIFICATION

Authors: LUGOSI G NOBEL A

Citation: G. Lugosi et A. Nobel, CONSISTENCY OF DATA-DRIVEN HISTOGRAM METHODS FOR DENSITY-ESTIMATION AND CLASSIFICATION, Annals of statistics, 24(2), 1996, pp. 687-706

CONCEPT-LEARNING USING COMPLEXITY REGULARIZATION

Authors: LUGOSI G ZEGER K

Citation: G. Lugosi et K. Zeger, CONCEPT-LEARNING USING COMPLEXITY REGULARIZATION, IEEE transactions on information theory, 42(1), 1996, pp. 48-54

LOWER BOUNDS IN PATTERN-RECOGNITION AND LEARNING

Authors: DEVROYE L LUGOSI G

Citation: L. Devroye et G. Lugosi, LOWER BOUNDS IN PATTERN-RECOGNITION AND LEARNING, Pattern recognition, 28(7), 1995, pp. 1011-1018

FIXED-RATE UNIVERSAL LOSSY SOURCE-CODING AND RATES OF CONVERGENCE FORMEMORYLESS SOURCES

Authors: LINDER T LUGOSI G ZEGER K

Citation: T. Linder et al., FIXED-RATE UNIVERSAL LOSSY SOURCE-CODING AND RATES OF CONVERGENCE FORMEMORYLESS SOURCES, IEEE transactions on information theory, 41(3), 1995, pp. 665-676

NONPARAMETRIC-ESTIMATION VIA EMPIRICAL RISK MINIMIZATION

Authors: LUGOSI G ZEGER K

Citation: G. Lugosi et K. Zeger, NONPARAMETRIC-ESTIMATION VIA EMPIRICAL RISK MINIMIZATION, IEEE transactions on information theory, 41(3), 1995, pp. 677-687

ON THE STRONG UNIVERSAL CONSISTENCY OF NEAREST-NEIGHBOR REGRESSION FUNCTION ESTIMATES

Authors: DEVROYE L GYORFI L KRZYZAK A LUGOSI G

Citation: L. Devroye et al., ON THE STRONG UNIVERSAL CONSISTENCY OF NEAREST-NEIGHBOR REGRESSION FUNCTION ESTIMATES, Annals of statistics, 22(3), 1994, pp. 1371-1385

RATES OF CONVERGENCE IN THE SOURCE-CODING THEOREM, IN EMPIRICAL QUANTIZER DESIGN, AND IN UNIVERSAL LOSSY SOURCE-CODING

Authors: LINDER T LUGOSI G ZEGER K

Citation: T. Linder et al., RATES OF CONVERGENCE IN THE SOURCE-CODING THEOREM, IN EMPIRICAL QUANTIZER DESIGN, AND IN UNIVERSAL LOSSY SOURCE-CODING, IEEE transactions on information theory, 40(6), 1994, pp. 1728-1740

ON THE POSTERIOR-PROBABILITY ESTIMATE OF THE ERROR RATE OF NONPARAMETRIC CLASSIFICATION RULES

Authors: LUGOSI G PAWLAK M

Citation: G. Lugosi et M. Pawlak, ON THE POSTERIOR-PROBABILITY ESTIMATE OF THE ERROR RATE OF NONPARAMETRIC CLASSIFICATION RULES, IEEE transactions on information theory, 40(2), 1994, pp. 475-481

FAST NEAREST-NEIGHBOR SEARCH IN DISSIMILARITY SPACES

Authors: FARAGO A LINDER T LUGOSI G

Citation: A. Farago et al., FAST NEAREST-NEIGHBOR SEARCH IN DISSIMILARITY SPACES, IEEE transactions on pattern analysis and machine intelligence, 15(9), 1993, pp. 957-962

STRONG UNIVERSAL CONSISTENCY OF NEURAL-NETWORK CLASSIFIERS

Authors: FARAGO A LUGOSI G

Citation: A. Farago et G. Lugosi, STRONG UNIVERSAL CONSISTENCY OF NEURAL-NETWORK CLASSIFIERS, IEEE transactions on information theory, 39(4), 1993, pp. 1146-1151

Risultati: 1-16 |