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6,886
Regularization networks and support vector machines
 Advances in Computational Mathematics
, 2000
"... Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization a ..."
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Cited by 366 (38 self)
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Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization
Regularization Theory and Neural Networks Architectures
 Neural Computation
, 1995
"... We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known Radial Ba ..."
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Cited by 395 (32 self)
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We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known Radial
Regular networks are determined by their trees
, 2009
"... Abstract. A rooted acyclic digraph N with labelled leaves displays a tree T when there exists a way to select a unique parent of each hybrid vertex resulting in the tree T. Let Tr(N) denote the set of all trees displayed by the network N. In general, there may be many other networks M such that Tr(M ..."
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Cited by 3 (0 self)
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(M) = Tr(N). A network is regular if it is isomorphic with its cover digraph. This paper shows that if N is regular, there is a procedure to reconstruct N given Tr(N). Hence if N and M are regular networks and Tr(N) = Tr(M), it follows that N = M, proving that a regular network is uniquely determined
Diagnosabilities of regular networks
 IEEE Transactions on Parallel and Distributed Systems
"... In this paper, we study diagnosabilities of multiprocessor systems under two diagnosis models: the PMC model and the comparison model. In each model, we further consider two different diagnosis strategies: the precise diagnosis strategy proposed by Preparata et al. and the pessimistic diagnosis stra ..."
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Cited by 3 (0 self)
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strategy proposed by Friedman. The main result of this paper is to determine diagnosabilities of regular networks with certain conditions, which include several widely used multiprocessor systems such as variants of hypercubes and many others.
Generalization Performance of Regularization Networks and Support . . .
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2001
"... We derive new bounds for the generalization error of kernel machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs make use of a viewpoint that is apparently novel in the field of statistical learning theory. The hy ..."
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Cited by 79 (17 self)
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We derive new bounds for the generalization error of kernel machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs make use of a viewpoint that is apparently novel in the field of statistical learning theory
Understanding Normal and Impaired Word Reading: Computational Principles in QuasiRegular Domains
 PSYCHOLOGICAL REVIEW
, 1996
"... We develop a connectionist approach to processing in quasiregular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic and phono ..."
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Cited by 613 (94 self)
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and phonological representations that capture better the relevant structure among the written and spoken forms of words. In a number of simulation experiments, networks using the new representations learn to read both regular and exception words, including lowfrequency exception words, and yet are still able
Error and attack tolerance of complex networks
, 2000
"... Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network [1]. C ..."
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Cited by 1013 (7 self)
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]. Complex communication networks [2] display a surprising degree of robustness: while key components regularly malfunction, local failures rarely lead to the loss of the global informationcarrying ability of the network. The stability of these and other complex systems is often attributed to the redundant
Capacity of Ad Hoc Wireless Networks
"... Early simulation experience with wireless ad hoc networks suggests that their capacity can be surprisingly low, due to the requirement that nodes forward each others’ packets. The achievable capacity depends on network size, traffic patterns, and detailed local radio interactions. This paper examine ..."
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Cited by 636 (14 self)
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forwarding and the effect on capacity for several simple configurations and traffic patterns. While 802.11 discovers reasonably good schedules, we nonetheless observe capacities markedly less than optimal for very simple chain and lattice networks with very regular traffic patterns. We validate some
Internet time synchronization: The network time protocol
, 1989
"... This memo describes the Network Time Protocol (NTP) designed to distribute time information in a large, diverse internet system operating at speeds from mundane to lightwave. It uses a returnabletime architecture in which a distributed subnet of time servers operating in a selforganizing, hierarchi ..."
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Cited by 628 (15 self)
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This memo describes the Network Time Protocol (NTP) designed to distribute time information in a large, diverse internet system operating at speeds from mundane to lightwave. It uses a returnabletime architecture in which a distributed subnet of time servers operating in a self
Imagenet classification with deep convolutional neural networks.
 In Advances in the Neural Information Processing System,
, 2012
"... Abstract We trained a large, deep convolutional neural network to classify the 1.2 million highresolution images in the ImageNet LSVRC2010 contest into the 1000 different classes. On the test data, we achieved top1 and top5 error rates of 37.5% and 17.0% which is considerably better than the pr ..."
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Cited by 1010 (11 self)
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Abstract We trained a large, deep convolutional neural network to classify the 1.2 million highresolution images in the ImageNet LSVRC2010 contest into the 1000 different classes. On the test data, we achieved top1 and top5 error rates of 37.5% and 17.0% which is considerably better than
Results 1  10
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6,886