9 research outputs found

    Heterogeneous neural networks: theory and applications

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    Aquest treball presenta una classe de funcions que serveixen de models neuronals generalitzats per ser usats en xarxes neuronals artificials. Es defineixen com una mesura de similitud que actúa com una definició flexible de neurona vista com un reconeixedor de patrons. La similitud proporciona una marc conceptual i serveix de cobertura unificadora de molts models neuronals de la literatura i d'exploració de noves instàncies de models de neurona. La visió basada en similitud porta amb naturalitat a integrar informació heterogènia, com ara quantitats contínues i discretes (nominals i ordinals), i difuses ó imprecises. Els valors perduts es tracten de manera explícita. Una neurona d'aquesta classe s'anomena neurona heterogènia i qualsevol arquitectura neuronal que en faci ús serà una Xarxa Neuronal Heterogènia.En aquest treball ens concentrem en xarxes neuronals endavant, com focus inicial d'estudi. Els algorismes d'aprenentatge són basats en algorisms evolutius, especialment extesos per treballar amb informació heterogènia. En aquesta tesi es descriu com una certa classe de neurones heterogènies porten a xarxes neuronals que mostren un rendiment molt satisfactori, comparable o superior al de xarxes neuronals tradicionals (com el perceptró multicapa ó la xarxa de base radial), molt especialment en presència d'informació heterogènia, usual en les bases de dades actuals.This work presents a class of functions serving as generalized neuron models to be used in artificial neural networks. They are cast into the common framework of computing a similarity function, a flexible definition of a neuron as a pattern recognizer. The similarity endows the model with a clear conceptual view and serves as a unification cover for many of the existing neural models, including those classically used for the MultiLayer Perceptron (MLP) and most of those used in Radial Basis Function Networks (RBF). These families of models are conceptually unified and their relation is clarified. The possibilities of deriving new instances are explored and several neuron models --representative of their families-- are proposed. The similarity view naturally leads to further extensions of the models to handle heterogeneous information, that is to say, information coming from sources radically different in character, including continuous and discrete (ordinal) numerical quantities, nominal (categorical) quantities, and fuzzy quantities. Missing data are also explicitly considered. A neuron of this class is called an heterogeneous neuron and any neural structure making use of them is an Heterogeneous Neural Network (HNN), regardless of the specific architecture or learning algorithm. Among them, in this work we concentrate on feed-forward networks, as the initial focus of study. The learning procedures may include a great variety of techniques, basically divided in derivative-based methods (such as the conjugate gradient)and evolutionary ones (such as variants of genetic algorithms).In this Thesis we also explore a number of directions towards the construction of better neuron models --within an integrant envelope-- more adapted to the problems they are meant to solve.It is described how a certain generic class of heterogeneous models leads to a satisfactory performance, comparable, and often better, to that of classical neural models, especially in the presence of heterogeneous information, imprecise or incomplete data, in a wide range of domains, most of them corresponding to real-world problems.Postprint (published version

    Fuzzy Mathematics

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    This book provides a timely overview of topics in fuzzy mathematics. It lays the foundation for further research and applications in a broad range of areas. It contains break-through analysis on how results from the many variations and extensions of fuzzy set theory can be obtained from known results of traditional fuzzy set theory. The book contains not only theoretical results, but a wide range of applications in areas such as decision analysis, optimal allocation in possibilistics and mixed models, pattern classification, credibility measures, algorithms for modeling uncertain data, and numerical methods for solving fuzzy linear systems. The book offers an excellent reference for advanced undergraduate and graduate students in applied and theoretical fuzzy mathematics. Researchers and referees in fuzzy set theory will find the book to be of extreme value

    A novel approach to handwritten character recognition

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    A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

    Design space exploration of associative memories using spiking neurons with respect to neuromorphic hardware implementations

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    Stöckel A. Design space exploration of associative memories using spiking neurons with respect to neuromorphic hardware implementations. Bielefeld: Universität Bielefeld; 2016.Artificial neural networks are well-established models for key functions of biological brains, such as low-level sensory processing and memory. In particular, networks of artificial spiking neurons emulate the time dynamics, high parallelisation and asynchronicity of their biological counterparts. Large scale hardware simulators for such networks – _neuromorphic_ computers – are developed as part of the Human Brain Project, with the ultimate goal to gain insights regarding the neural foundations of cognitive processes. In this thesis, we focus on one key cognitive function of biological brains, associative memory. We implement the well-understood Willshaw model for artificial spiking neural networks, thoroughly explore the design space for the implementation, provide fast design space exploration software and evaluate our implementation in software simulation as well as neuromorphic hardware. Thereby we provide an approach to manually or automatically infer viable parameters for an associative memory on different hardware and software platforms. The performance of the associative memory was found to vary significantly between individual neuromorphic hardware platforms and numerical simulations. The network is thus a suitable benchmark for neuromorphic systems

    On the application of neural networks to symbol systems.

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    While for many years two alternative approaches to building intelligent systems, symbolic AI and neural networks, have each demonstrated specific advantages and also revealed specific weaknesses, in recent years a number of researchers have sought methods of combining the two into a unified methodology which embodies the benefits of each while attenuating the disadvantages. This work sets out to identify the key ideas from each discipline and combine them into an architecture which would be practically scalable for very large network applications. The architecture is based on a relational database structure and forms the environment for an investigation into the necessary properties of a symbol encoding which will permit the singlepresentation learning of patterns and associations, the development of categories and features leading to robust generalisation and the seamless integration of a range of memory persistencies from short to long term. It is argued that if, as proposed by many proponents of symbolic AI, the symbol encoding must be causally related to its syntactic meaning, then it must also be mutable as the network learns and grows, adapting to the growing complexity of the relationships in which it is instantiated. Furthermore, it is argued that in order to create an efficient and coherent memory structure, the symbolic encoding itself must have an underlying structure which is not accessible symbolically; this structure would provide the framework permitting structurally sensitive processes to act upon symbols without explicit reference to their content. Such a structure must dictate how new symbols are created during normal operation. The network implementation proposed is based on K-from-N codes, which are shown to possess a number of desirable qualities and are well matched to the requirements of the symbol encoding. Several networks are developed and analysed to exploit these codes, based around a recurrent version of the non-holographic associati ve memory of Willshaw, et al. The simplest network is shown to have properties similar to those of a Hopfield network, but the storage capacity is shown to be greater, though at a cost of lower signal to noise ratio. Subsequent network additions break each K-from-N pattern into L subsets, each using D-from-N coding, creating cyclic patterns of period L. This step increases the capacity still further but at a cost of lower signal to noise ratio. The use of the network in associating pairs of input patterns with any given output pattern, an architectural requirement, is verified. The use of complex synaptic junctions is investigated as a means to increase storage capacity, to address the stability-plasticity dilemma and to implement the hierarchical aspects of the symbol encoding defined in the architecture. A wide range of options is developed which allow a number of key global parameters to be traded-off. One scheme is analysed and simulated. A final section examines some of the elements that need to be added to our current understanding of neural network-based reasoning systems to make general purpose intelligent systems possible. It is argued that the sections of this work represent pieces of the whole in this regard and that their integration will provide a sound basis for making such systems a reality

    A novel approach to handwritten character recognition

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    A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

    Navigieren

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    Prof. Dr. Jens Schröter, Christoph Borbach, Max Kanderske und Prof. Dr. Benjamin Beil sind Herausgeber der Reihe. Die Herausgeber*innen der einzelnen Hefte sind renommierte Wissenschaftler*innen aus dem In- und Ausland.Navigieren ist längst kein Unikum professionalisierter Seefahrer:innen mehr, sondern als Smartphone- und Browser-Praktik fester Bestandteil des vernetzten digitalen Alltags. Da Wegfindungen durch On- und Offline-Räume navigationsspezifische Formen von Medienkompetenz voraussetzen und hervorbringen, fordern sie die Intensivierung der medienkulturwissenschaftlichen Beschäftigung mit den situierten und technisierten Medienpraktiken der Navigation geradezu heraus. Die Ausgabe nimmt diesen Befund zum Anlass, polyperspektivische Zugänge zum »Navigieren« vorzustellen. Die körper-, kultur- und medientechnischen Facetten des Navigierens stehen dabei ebenso im Fokus wie ihre historischen Ausgestaltungen, die Arbeit am und im Datenmaterial von Navigationsmedien und die Theoretisierung postdigitaler Sensor-Medien-Kulturen, die dem Umstand Rechnung trägt, dass es nicht allein Daten, Dinge und Körper sind, die es zu navigieren gilt, sondern zunehmend nicht-menschliche Akteure selbst zielgerichtete Raumdurchquerungen praktizieren. Fehlte es in der (deutschsprachigen) Medienkulturwissenschaft bislang an einer Bündelung heterogener navigationsspezifischer Forschungsarbeiten, gibt diese Ausgabe einen Überblick über das Feld, seine Forscher:innen und Fragestellungen. Denn trotz des Spatial Turns in den Humanities und der gegenwärtigen Konjunktur geomedialer Arbeiten, scheint die synthetisierende Fokussierung auf Medien und Praktiken des Navigierens in historischer, ethnografischer, technischer und theoretischer Perspektive bislang ein Desiderat darzustellen.Navigation is no longer unique to the context of professional seafaring, but has become an integral part of networked digital everyday life enabled through smartphones and web browsers. Indeed, finding one’s way through online and offline spaces increasingly presupposes and produces specific forms of media competence one could call »navigational«. In this, a ›media cultural studies‹ perspective on the situated and ›technologized‹ media practices of navigation becomes imperative to understanding the contemporary media landscape. Issue 1/22 of Navigationen answers this call by presenting polyperspectival approaches to »navigating«. The contributions discuss the bodily, cultural, and media-technical facets of navigation, as well as its historical forms, the work on and in the data produced by and with navigational media, and the theorization of post-digital ›sensor media cultures‹. In doing so, the issue acknowledges that not only do data, things, and bodies need to be ›navigated‹ in the context of logistics, but that the increasingly autonomous wayfinding processes of non-human actors change the notion of navigation itself. As (German language) media cultural studies has so far lacked a convincing compilation of heterogeneous approaches to studying navigation, this issue provides an overview of the field, its researchers and questions. Despite the spatial turn in the humanities and a recent surge in geomedia studies, an approach towards the media and practices of navigation that combines historical, ethnographic, technical and theoretical perspectives, has remained a desideratum until now. The issue fills this gap
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