5,460 research outputs found
Diffuse pattern learning with Fuzzy ARTMAP and PASS
Fuzzy ARTMAP is compared to a classifier system (CS) called PASS (predictive adaptive sequential system). Previously reported results in a benchmark classification task suggest that Fuzzy ARTMAP systems perform better and are more parsimonious than systems based on the CS architecture. The tasks considered here differ from ordinary classificatory tasks in the amount of output uncertainty associated with input categories. To be successful, learning systems must identify not only correct input categories, but also the most likely outputs for those categories. Performance under various types of diffuse patterns is investigated using a simulated scenario
Locally linear approximation for Kernel methods : the Railway Kernel
In this paper we present a new kernel, the Railway Kernel, that works properly for
general (nonlinear) classification problems, with the interesting property that acts
locally as a linear kernel. In this way, we avoid potential problems due to the use of a
general purpose kernel, like the RBF kernel, as the high dimension of the induced
feature space. As a consequence, following our methodology the number of support
vectors is much lower and, therefore, the generalization capability of the proposed
kernel is higher than the obtained using RBF kernels. Experimental work is shown to
support the theoretical issues
Rejoinder to "Support Vector Machines with Applications"
Rejoinder to ``Support Vector Machines with Applications'' [math.ST/0612817]Comment: Published at http://dx.doi.org/10.1214/088342306000000501 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Representing functional data in reproducing Kernel Hilbert Spaces with applications to clustering and classification
Functional data are difficult to manage for many traditional statistical techniques given their very high (or intrinsically infinite) dimensionality. The reason is that functional data are essentially functions and most algorithms are designed to work with (low) finite-dimensional vectors. Within this context we propose techniques to obtain finitedimensional representations of functional data. The key idea is to consider each functional curve as a point in a general function space and then project these points onto a Reproducing Kernel Hilbert Space with the aid of Regularization theory. In this work we describe the projection method, analyze its theoretical properties and propose a model selection procedure to select appropriate Reproducing Kernel Hilbert spaces to project the functional data.Functional data, Reproducing, Kernel Hilbert Spaces, Regularization theory
Vote-boosting ensembles
Vote-boosting is a sequential ensemble learning method in which the
individual classifiers are built on different weighted versions of the training
data. To build a new classifier, the weight of each training instance is
determined in terms of the degree of disagreement among the current ensemble
predictions for that instance. For low class-label noise levels, especially
when simple base learners are used, emphasis should be made on instances for
which the disagreement rate is high. When more flexible classifiers are used
and as the noise level increases, the emphasis on these uncertain instances
should be reduced. In fact, at sufficiently high levels of class-label noise,
the focus should be on instances on which the ensemble classifiers agree. The
optimal type of emphasis can be automatically determined using
cross-validation. An extensive empirical analysis using the beta distribution
as emphasis function illustrates that vote-boosting is an effective method to
generate ensembles that are both accurate and robust
Enabling Practical IPsec authentication for the Internet
On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops (First International Workshop on Information Security (IS'06), OTM Federated Conferences and workshops). Montpellier, Oct,/Nov. 2006There is a strong consensus about the need for IPsec, although its use is not widespread for end-to-end communications. One of the main reasons for this is the difficulty for authenticating two end-hosts that do not share a secret or do not rely on a common Certification Authority. In this paper we propose a modification to IKE to use reverse DNS and DNSSEC (named DNSSEC-to-IKE) to provide end-to-end authentication to Internet hosts that do not share any secret, without requiring the deployment of a new infrastructure. We perform a comparative analysis in terms of requirements, provided security and performance with state-of-the-art IKE authentication methods and with a recent proposal for IPv6 based on CGA. We conclude that DNSSEC-to-IKE enables the use of IPsec in a broad range of scenarios in which it was not applicable, at the price of offering slightly less security and incurring in higher performance costs.Universidad de Montpellier IIPublicad
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