Results 11  20
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951,356
D.: Margin distribution and learning algorithms
 In: Proceedings of the 12th Conference on Computational Learning Theory
, 1999
"... Recent theoretical results have shown that improved bounds on generalization error of classiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, algorithms used in practice do not make use of the margin distribution and are driven ..."
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Cited by 11 (0 self)
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Recent theoretical results have shown that improved bounds on generalization error of classiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, algorithms used in practice do not make use of the margin distribution
THAT DO NOT DEPEND ON THE MARGINAL DISTRIBUTIONS
, 2008
"... We discuss properties that association coefficients may have in general, e.g., zero value under statistical independence, and we examine coefficients for 2 × 2 tables with respect to these properties. Furthermore, we study a family of coefficients that are linear transformations of the observed prop ..."
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proportion of agreement given the marginal probabilities. This family includes the phi coefficient and Cohen’s kappa. The main result is that the linear transformations that set the value under independence at zero and the maximum value at unity, transform all coefficients in this family into the same
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
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Cited by 560 (20 self)
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We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class
Empirical margin distributions and bounding the generalization error of combined classifiers
 Ann. Statist
, 2002
"... Dedicated to A.V. Skorohod on his seventieth birthday We prove new probabilistic upper bounds on generalization error of complex classifiers that are combinations of simple classifiers. Such combinations could be implemented by neural networks or by voting methods of combining the classifiers, such ..."
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Cited by 160 (11 self)
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, such as boosting and bagging. The bounds are in terms of the empirical distribution of the margin of the combined classifier. They are based on the methods of the theory of Gaussian and empirical processes (comparison inequalities, symmetrization method, concentration inequalities) and they improve previous
Stochastic control with fixed marginal distributions §
, 2006
"... Running title: Stochastic control with fixed marginal distributions We briefly describe the socalled MongeKantorovich Problem (MKP for short) which is often referred to as an optimal mass transportation problem and study the stochastic optimal control problem (SOCP for short) with fixed initial an ..."
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Running title: Stochastic control with fixed marginal distributions We briefly describe the socalled MongeKantorovich Problem (MKP for short) which is often referred to as an optimal mass transportation problem and study the stochastic optimal control problem (SOCP for short) with fixed initial
Powerlaw distributions in empirical data
 ISSN 00361445. doi: 10.1137/ 070710111. URL http://dx.doi.org/10.1137/070710111
, 2009
"... Powerlaw distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and manmade phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur in the t ..."
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Cited by 589 (7 self)
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Powerlaw distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and manmade phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Nested Transactions: An Approach to Reliable Distributed Computing
, 1981
"... Distributed computing systems are being built and used more and more frequently. This distributod computing revolution makes the reliability of distributed systems an important concern. It is fairly wellunderstood how to connect hardware so that most components can continue to work when others are ..."
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Cited by 527 (1 self)
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Distributed computing systems are being built and used more and more frequently. This distributod computing revolution makes the reliability of distributed systems an important concern. It is fairly wellunderstood how to connect hardware so that most components can continue to work when others
Distributed hierarchical processing in the primate cerebral cortex
 Cereb Cortex
, 1991
"... In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a comput ..."
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Cited by 901 (6 self)
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In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visualassociation areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning al ..."
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Cited by 560 (15 self)
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We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning
Results 11  20
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951,356