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30 results on '"Talavera, Isneri"'

2. A New Iris Recognition Approach Based on a Functional Representation

Author

Porro-Muñoz, Dania, Silva-Mata, Francisco José, Mendiola-Lau, Victor, Hernández, Noslen, Talavera, Isneri, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ruiz-Shulcloper, José, editor, and Sanniti di Baja, Gabriella, editor

Published
2013
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3. Missing Values in Dissimilarity-Based Classification of Multi-way Data

Author

Porro-Muñoz, Diana, Duin, Robert P. W., Talavera, Isneri, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ruiz-Shulcloper, José, editor, and Sanniti di Baja, Gabriella, editor

Published
2013
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4. A Non Bayesian Predictive Approach for Functional Calibration

Author

Hernández, Noslen, Biscay, Rolando J., Talavera, Isneri, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Alvarez, Luis, editor, Mejail, Marta, editor, Gomez, Luis, editor, and Jacobo, Julio, editor

Published
2012
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5. A Study on the Influence of Shape in Classifying Small Spectral Data Sets

Author

Porro-Muñoz, Diana, Duin, Robert P. W., Talavera, Isneri, Orozco-Alzate, Mauricio, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Pelillo, Marcello, editor, and Hancock, Edwin R., editor

Published
2011
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6. A Functional Density-Based Nonparametric Approach for Statistical Calibration

Author

Hernández, Noslen, Biscay, Rolando J., Villa-Vialaneix, Nathalie, Talavera, Isneri, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bloch, Isabelle, editor, and Cesar, Roberto M., Jr., editor

Published
2010
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7. The Dissimilarity Representation as a Tool for Three-Way Data Classification: A 2D Measure

Author

Porro-Muñoz, Diana, Duin, Robert P. W., Orozco-Alzate, Mauricio, Talavera, Isneri, Londoño-Bonilla, John Makario, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hancock, Edwin R., editor, Wilson, Richard C., editor, Windeatt, Terry, editor, Ulusoy, Ilkay, editor, and Escolano, Francisco, editor

Published
2010
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8. The Representation of Chemical Spectral Data for Classification

Author

Porro, Diana, Duin, Robert W., Talavera, Isneri, Hdez, Noslen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bayro-Corrochano, Eduardo, editor, and Eklundh, Jan-Olof, editor

Published
2009
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9. Support Vector Regression Methods for Functional Data

Author

Hernández, Noslen, Biscay, Rolando J., Talavera, Isneri, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Rueda, Luis, editor, and Mery, Domingo, editor

Published
2007
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19. A Simulation Study of Functional Density-Based Inverse Regression

Author

Hernandez, Noslen, Biscay, Rolando, Villa-Vialaneix, Nathalie, Talavera, Isneri, Centro de Aplicaciones de Tecnologías de Avanzada (CENATAV), CENATAV, Departamento de Estadística Universidad de Valparaíso (IMFAV-DEUV), Universidad de Valparaiso [Chile], Institute of Mathematics, Physics and Cybernetics, University of Havana (Universidad de la Habana) (UH), Institut de Mathématiques de Toulouse UMR5219 (IMT), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), IUT - Département STID - Carcassonne (UPVD), Université de Perpignan Via Domitia (UPVD), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]
Abstract

National audience; In this paper a new nonparametric functional method is introduced for predicting a scalar random variable $Y$ on the basis of a functional random variable $X$. The prediction has the form of a weighted average of the training data $y_{i}$, where the weights are determined by the conditional probability density of $X$ given $Y=y_{i}$, which is assumed to be Gaussian. In this way such a conditional probability density is incorporated as a key information into the estimator. Contrary to some previous approaches, no assumption about the dimensionality of $E(X|Y=y)$ or about the distribution of $X$ is required. The new proposal is computationally simple and easy to implement. Its performance is assessed through a simulation study.

Published
2011

29. Support Vector Regression Methods for Functional Data.

Author

Hutchison, David, Kanade, Takeo, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Rueda, Luis, Mery, Domingo, Kittler, Josef, Hernández, Noslen, Biscay, Rolando J., and Talavera, Isneri

Abstract

Many regression tasks in practice dispose in low gear instance of digitized functions as predictor variables. This has motivated the development of regression methods for functional data. In particular, Naradaya-Watson Kernel (NWK) and Radial Basis Function (RBF) estimators have been recently extended to functional nonparametric regression models. However, these methods do not allow for dimensionality reduction. For this purpose, we introduce Support Vector Regression (SVR) methods for functional data. These are formulated in the framework of approximation in reproducing kernel Hilbert spaces. On this general basis, some of its properties are investigated, emphasizing the construction of nonnegative definite kernels on functional spaces. Furthermore, the performance of SVR for functional variables is shown on a real world benchmark spectrometric data set, as well as comparisons with NWK and RBF methods. Good predictions were obtained by these three approaches, but SVR achieved in addition about 20% reduction of dimensionality. [ABSTRACT FROM AUTHOR]

Published
2008
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30. A SIMULATION STUDY OF FUNCTIONAL DENSITY-BASED INVERSE REGRESSION.

Author

Hern´andez, Noslen, Biscay, Rolando J., Villa-Vialaneix, Nathalie, and Talavera, Isneri

Subjects
*NONPARAMETRIC statistics, *REGRESSION analysis, *FUNCTIONAL analysis, *GAUSSIAN distribution, *RANDOM variables
Abstract

In this paper a new nonparametric functional regression method is introduced for predicting a scalar random variable Y on the basis of a functional random variable X. The prediction has the form of a weighted average of the training data yi, where the weights are determined by the conditional probability density of X given Y = yi, which is assumed to be Gaussian. In this way such a conditional probability density is incorporated as a key information into the estimator. Contrary to some previous approaches, no assumption about the dimensionality of E(XΙY = y) or about the distribution of X is required. The new proposal is computationally simple and easy to implement. Its performance is assessed through a simulation study. [ABSTRACT FROM AUTHOR]

Published
2011

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