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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Analysis of quantum functions
 in Proceedings of the 19th International Conference on Foundations of Software Technology and Theoretical Computer Science, Lecture Notes in Computer Science, Vol.1738
, 1999
"... Abstract. Quantum functions are functions that are defined in terms of quantum mechanical computation. Besides quantum computable functions, we study quantum probability functions, which compute the acceptance probability of quantum computation. We also investigate quantum gap functions, which compu ..."
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Cited by 7 (6 self)
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Abstract. Quantum functions are functions that are defined in terms of quantum mechanical computation. Besides quantum computable functions, we study quantum probability functions, which compute the acceptance probability of quantum computation. We also investigate quantum gap functions, which
Analysis of Quantum Functions ⋆ (Preliminary Version)
, 1999
"... Abstract. Quantum functions are functions that are defined in terms of quantum mechanical computation. Besides quantum computable functions, we study quantum probability functions, which compute the acceptance probability of quantum computation. We also investigate quantum gap functions, which compu ..."
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Abstract. Quantum functions are functions that are defined in terms of quantum mechanical computation. Besides quantum computable functions, we study quantum probability functions, which compute the acceptance probability of quantum computation. We also investigate quantum gap functions, which
AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
 Computers and Biomedical Research
, 1996
"... email rwcoxmcwedu A package of computer programs for analysis and visualization of threedimensional human brain functional magnetic resonance imaging FMRI results is described The software can color overlay neural activation maps onto higher resolution anatomical scans Slices in each cardinal pl ..."
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Cited by 807 (3 self)
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email rwcoxmcwedu A package of computer programs for analysis and visualization of threedimensional human brain functional magnetic resonance imaging FMRI results is described The software can color overlay neural activation maps onto higher resolution anatomical scans Slices in each cardinal
Performance Analysis of the IEEE 802.11 Distributed Coordination Function
, 2000
"... Recently, the IEEE has standardized the 802.11 protocol for Wireless Local Area Networks. The primary medium access control (MAC) technique of 802.11 is called distributed coordination function (DCF). DCF is a carrier sense multiple access with collision avoidance (CSMA/CA) scheme with binary slott ..."
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Cited by 1869 (1 self)
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Recently, the IEEE has standardized the 802.11 protocol for Wireless Local Area Networks. The primary medium access control (MAC) technique of 802.11 is called distributed coordination function (DCF). DCF is a carrier sense multiple access with collision avoidance (CSMA/CA) scheme with binary
An analysis of transformations
 Journal of the Royal Statistical Society. Series B (Methodological
, 1964
"... In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedasti ..."
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Cited by 1067 (3 self)
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In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal
Convex Analysis
, 1970
"... In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a lo ..."
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Cited by 5411 (68 self)
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In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a
Keying hash functions for message authentication
, 1996
"... The use of cryptographic hash functions like MD5 or SHA for message authentication has become a standard approach inmanyInternet applications and protocols. Though very easy to implement, these mechanisms are usually based on ad hoc techniques that lack a sound security analysis. We present new cons ..."
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Cited by 611 (39 self)
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The use of cryptographic hash functions like MD5 or SHA for message authentication has become a standard approach inmanyInternet applications and protocols. Though very easy to implement, these mechanisms are usually based on ad hoc techniques that lack a sound security analysis. We present new
Probabilistic Principal Component Analysis
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation of paramet ..."
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Cited by 709 (5 self)
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of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach
Results 1  10
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145,133