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

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

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

Organoides: qué son y para qué sirven. Organoides colónicos y cáncer

Conferencia sobre un tema de actualidad impartida por un especialista de gran prestigio nacional e internacional y con excepcionales capacidades como divulgador de la ciencia.Los organoides son cultivos celulares tridimensionales, inicialmente de origen epitelial, generados por células stem/troncales (adultas, embrionales o inducidas) que reproducen parcialmente las características de sus tejidos de origen. Constituyen un sistema más apropiado que las clásicas líneas celulares de crecimiento bidimensional en plástico para estudios de biologia del desarrollo y celular e histología, así como de procesos de tumorogénesis y de medicina regenerativa. Revisaremos los procesos de establecimiento de los primeros organoides a partir de intestino delgado y luego colon, así como los numerosos estudios que indican su utilidad para el estudio del cáncer de colorrectal y el ensayo de drogas antitumorales y medicina personalizada.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Departamento de Biología Molecular y Bioquímica Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Máster de Biología Celular y Molecula

Compound key word generation from document databases using a hierarchical clustering art model

The growing availability of databases on the information highways motivates the development of new processing tools able to deal with a heterogeneous and changing information environment. A highly desirable feature of data processing systems handling this type of information is the ability to automatically extract its own key words. In this paper we address the specific problem of creating semantic term associations from a text database. The proposed method uses a hierarchical model made up of Fuzzy Adaptive Resonance Theory (ART) neural networks. First, the system uses several Fuzzy ART modules to cluster isolated words into semantic classes, starting from the database raw text. Next, this knowledge is used together with coocurrence information to extract semantically meaningful term associations. These associations are asymmetric and one-to-many due to the polisemy phenomenon. The strength of the associations between words can be measured numerically. Besides this, they implicitly define a hierarchy between descriptors. The underlying algorithm is appropriate for employment on large databases. The operation of the system is illustrated on several real databases

The Development of Novel N-Heterocyclic Carbenes and Tools for Assessing Structural Variation Effects Upon Catalyst Reactivity

N-Heterocyclic carbenes (NHCs) are an important class of compounds responsible for a wide variety of chemical transformations. NHCs may be used as organocatalysts that permit non-traditional carbon carbon bond formations due to their renowned ability to invert the electrophilic character of aldehyde carbonyl groups, a concept otherwise known as polarity reversal or umpolung reactivity. Despite their ubiquity with respect to accessing the umpolung of aldehydes, fundamental studies of these reactive species are still rather limited and narrow in scope. As a result, clarifying and solving problems relevant to umpolung-themed asymmetric catalysis becomes quite challenging. In this regard, our work has been focused on a three-pronged approach towards providing a more unified understanding of these complex catalytic systems. First, we describe the synthesis of unprecedented carboxylate-tethered triazolium NHCs and use them in the intramolecular Stetter reaction to understand their function. Second, we describe the acidities of a broad range of both chiral and achiral NHCs that have never had their acidities assessed before and use them to construct the first linear free-energy relationships of their kind. Finally, we develop a simple and noninvasive experimental protocol in which we can quickly benchmark the performance of a series of chiral catalysts by way of single competition experiments. We anticipate that these studies will have direct implications on the development of novel NHC-catalyzed reactions

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