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10,442
Kernel independent component analysis
 Journal of Machine Learning Research
, 2002
"... We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical propert ..."
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Cited by 464 (24 self)
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We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical
On the Truncated Kernel Function
 JOURNAL OF INTEGER SEQUENCES, VOL. 15 (2012), ARTICLE 12.3.2
, 2012
"... We study properties of the truncated kernel function γ2 defined on integers n ≥ 2 by γ2(n) = γ(n)/P(n), where γ(n) = ∏ pn p is the wellknown kernel function and P(n) is the largest prime factor of n. In particular, we show that the maximal order of γ2(n) for n ≤ x is (1 + o(1))x/log x as x → ∞ ..."
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We study properties of the truncated kernel function γ2 defined on integers n ≥ 2 by γ2(n) = γ(n)/P(n), where γ(n) = ∏ pn p is the wellknown kernel function and P(n) is the largest prime factor of n. In particular, we show that the maximal order of γ2(n) for n ≤ x is (1 + o(1))x/log x as x
On the algorithmic implementation of multiclass kernelbased vector machines
 Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernelbased vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
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Cited by 559 (13 self)
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In this paper we describe the algorithmic implementation of multiclass kernelbased vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic
On µkernel construction
 Symposium on Operating System Principles
, 1995
"... From a softwaretechnology point of view, thekernel concept is superior to large integrated kernels. On the other hand, it is widely believed that (a)kernel based systems are inherently inefficient and (b) they are not sufficiently flexible. Contradictory to this belief, we show and support by doc ..."
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Cited by 429 (25 self)
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by documentary evidence that inefficiency and inflexibility of currentkernels is not inherited from the basic idea but mostly from overloading the kernel and/or from improper implementation. Based on functional reasons, we describe some concepts which must be implemented by akernel and illustrate
Quadrature domains and kernel function zipping
 ARKIV FÖR MATEMATIK 43
, 2005
"... It is proved that quadrature domains are ubiquitous in a very strong sense in the realm of smoothly bounded multiply connected domains in the plane. In fact, they are so dense that one might as well assume that any given smooth domain one is dealing with is a quadrature domain, and this allows acces ..."
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Cited by 11 (3 self)
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access to a host of strong conditions on the classical kernel functions associated to the domain. Following this string of ideas leads to the discovery that the Bergman kernel can be “zipped ” down to a strikingly small data set. It is also proved that the kernel functions associated to a quadrature
Scheduler Activations: Effective Kernel Support for the UserLevel Management of Parallelism
 ACM Transactions on Computer Systems
, 1992
"... Threads are the vehicle,for concurrency in many approaches to parallel programming. Threads separate the notion of a sequential execution stream from the other aspects of traditional UNIXlike processes, such as address spaces and I/O descriptors. The objective of this separation is to make the expr ..."
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Cited by 475 (21 self)
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, as currently conceived, are the wrong abstraction on which to support user level management of parallelism. Finally, we describe the design, implementation, and performance of a new kernel interface and userlevel thread package that together provide the same functionality as kernel threads without compromis
Inductive regularized learning of kernel functions
"... In this paper we consider the fundamental problem of semisupervised kernel function learning. We first propose a general regularized framework for learning a kernel matrix, and then demonstrate an equivalence between our proposed kernel matrix learning framework and a general linear transformatio ..."
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Cited by 17 (1 self)
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In this paper we consider the fundamental problem of semisupervised kernel function learning. We first propose a general regularized framework for learning a kernel matrix, and then demonstrate an equivalence between our proposed kernel matrix learning framework and a general linear
Averaging of kernel functions
 in: European Symposium on Artificial Neural Networks (ESANN 2012
"... Abstract. In kernelbased machines, the integration of several kernels to build more flexible learning methods is a promising avenue for research. In particular, in Multiple Kernel Learning a compound kernel is build by learning a kernel that is the weighted mean of several sources. We show in this ..."
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Cited by 1 (0 self)
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Abstract. In kernelbased machines, the integration of several kernels to build more flexible learning methods is a promising avenue for research. In particular, in Multiple Kernel Learning a compound kernel is build by learning a kernel that is the weighted mean of several sources. We show
EVOLUTIONARY OPTIMISATION OF KERNEL FUNCTIONS FOR SVMS
"... Abstract. The kernelbased classifiers use one of the classical kernels, but the realworld applications have emphasized the need to consider a new kernel function in order to boost the classification accuracy by a better adaptation of the kernel function to the characteristics of the data. Our pur ..."
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Abstract. The kernelbased classifiers use one of the classical kernels, but the realworld applications have emphasized the need to consider a new kernel function in order to boost the classification accuracy by a better adaptation of the kernel function to the characteristics of the data. Our
Results 11  20
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10,442