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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Discovering subgroups by means of genetic programming

Discovering subgroups by means of genetic programming

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16th European Conference on Genetic Programming, EuroGP 2013

作者： Luna, José M. Romero, José Raúl Romero, Cristóbal Ventura, Sebastián Dept. of Computer Science and Numerical Analysis University of Cordoba Rabanales Campus 14071 Cordoba Spain

ISBN: (纸本)9783642372063

This paper deals with the problem of discovering subgroups in data by means of a grammar guided genetic programming algorithm, each subgroup including a set of related patterns. The proposed algorithm combines the requirements of discovering comprehensible rules with the ability of mining expressive and flexible solutions thanks to the use of a context-free grammar. A major characteristic of this algorithm is the small number of parameters required, so the mining process is easy for end-users. The algorithm proposed is compared with existing subgroup discovery evolutionary algorithms. The experimental results reveal the excellent behaviour of this algorithm, discovering comprehensible subgroups and behaving better than the other algorithms. The conclusions obtained were reinforced through a series of non-parametric tests. © 2013 Springer-Verlag.

关键词： Data mining

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Kernelizing the proportional odds model through the empirical kernel mapping

Kernelizing the proportional odds model through the empirica...

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12th International Work-Conference on Artificial Neural Networks, IWANN 2013

作者： Pérez-Ortiz, María Gutiérrez, Pedro Antonio Cruz-Ramírez, Manuel Sánchez-Monedero, Javier Hervás-Martínez, Cesar Dept. of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus 14071 Córdoba Spain

ISBN: (纸本)9783642386787

The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper explores the notion of kernel trick and empirical feature space in order to reformulate the most widely used linear ordinal classification algorithm (the Proportional Odds Model or POM) to perform nonlinear decision regions. The proposed method seems to be competitive with other state-of-the-art algorithms and significantly improves the original POM algorithm when using 8 ordinal datasets. Specifically, the capability of the methodology to handle nonlinear decision regions has been proven by the use of a non-linearly separable toy dataset. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Toys

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Evolutionary ordinal extreme learning machine

Evolutionary ordinal extreme learning machine

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8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013

作者： Sánchez-Monedero, Javier Gutiérrez, Pedro Antonio Hervás-Martínez, Cesar University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus 14071 - Córdoba Spain

ISBN: (纸本)9783642408458

Recently the ordinal extreme learning machine (ELMOR) algorithm has been proposed to adapt the extreme learning machine (ELM) algorithm to ordinal regression problems (problems where there is an order arrangement between categories). In addition, the ELM standard model has the drawback of needing many hidden layer nodes in order to achieve suitable performance. For this reason, several alternatives have been proposed, such as the evolutionary extreme learning machine (EELM). In this article we present an evolutionary ELMOR that improves the performance of ELMOR and EELM for ordinal regression. The model is integrated in the differential evolution algorithm of EELM, and it is extended to allow the use of a continuous weighted RMSE fitness function which is proposed to guide the optimization process. This favors classifiers which predict labels as close as possible (in the ordinal scale) to the real one. The experiments include eight datasets, five methods and three specific performance metrics. The results show the performance improvement of this type of neural networks for specific metrics which consider both the magnitude of errors and class imbalance. © 2013 Springer-Verlag.

关键词： Regression analysis

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An n-spheres based synthetic data generator for supervised classification

An n-spheres based synthetic data generator for supervised c...

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12th International Work-Conference on Artificial Neural Networks, IWANN 2013

作者： Sánchez-Monedero, Javier Gutiérrez, Pedro Antonio Pérez-Ortiz, María Hervás-Martínez, César Dept. of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus 14071 - Córdoba Spain

ISBN: (纸本)9783642386787

Synthetic datasets can be useful in a variety of situations, specifically when new machine learning models and training algorithms are developed or when trying to seek the weaknesses of an specific method. In contrast to real-world data, synthetic datasets provide a controlled environment for analysing concrete critic points such as outlier tolerance, data dimensionality influence and class imbalance, among others. In this paper, a framework for synthetic data generation is developed with special attention to pattern order in the space, data dimensionality, class overlapping and data multimodality. Variables such as position, width and overlapping of data distributions in the n-dimensional space are controlled by considering them as n-spheres. The method is tested in the context of ordinal regression, a paradigm of classification where there is an order arrangement between categories. The contribution of the paper is the full control over data topology and over a set of relevant statistical properties of the data. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Classification (of information)

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Borderline kernel based over-sampling

Borderline kernel based over-sampling

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8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013

作者： Pérez-Ortiz, María Gutiérrez, Pedro Antonio Hervás-Martínez, César University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus 14071 - Córdoba Spain

ISBN: (纸本)9783642408458

Nowadays, the imbalanced nature of some real-world data is receiving a lot of attention from the pattern recognition and machine learning communities in both theoretical and practical aspects, giving rise to different promising approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with kernel classifiers, that operate in the feature space induced by a kernel function. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the feature space and therefore preserves its structure) to derive a kernel-based synthetic over-sampling technique based on borderline instances which are considered as crucial for establishing the decision boundary. Therefore, the proposed methodology would maintain the main properties of the kernel mapping while reinforcing the decision boundaries induced by a kernel machine. The results show that the proposed method achieves better results than the same borderline over- sampling method applied in the original input space. © 2013 Springer-Verlag.

关键词： Pattern recognition

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A novel component identification approach using evolutionary programming

A novel component identification approach using evolutionary...

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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Ramírez, Aurora Romero, José Raúl Ventura, Sebastián Dept. of Computer Science and Numerical Analysis Rabanales Campus University of Córdoba 14071 Córdoba Spain

ISBN: (纸本)9781450319645

Component identification is a critical phase in software architecture analysis to prevent later errors and control the project time and budget. Obtaining the most appropriate architecture according to predetermined design criteria can be treated as an optimization problem, especially since the appearance of the Search Based Software Engineering, and its combination with bio-inspired metaheuristics. In this work, an evolutionary programming (EP) algorithm is used to identify components, based on a novel and comprehensible representation of software architectures.

关键词： Budget control

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Synthetic over-sampling in the empirical feature space

Synthetic over-sampling in the empirical feature space

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21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Hervás-Martínez, C. University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus Albert Einstein building 14071 Córdoba Spain

ISBN: (纸本)9782874190810

The imbalanced nature of some real-world data is one of the current challenges for machine learning, giving rise to different approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with the kernel classifiers, which make use of the feature space. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the feature space) to develop a kernel-based synthetic over-sampling technique, which maintains the main properties of the kernel mapping. The proposal achieves better results than the same oversampling method applied to the original input space.

关键词： Learning systems

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Multi-scale support vector machine optimization by kernel target-alignment

Multi-scale support vector machine optimization by kernel ta...

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21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Sánchez-Monedero, J. Hervás-Martínez, C. University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus Albert Einstein building 14071 - Córdoba Spain

ISBN: (纸本)9782874190810

The problem considered is the optimization of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, and through the use of evolutionary or gradient descent approaches, which explicitly train the learning machine and thereby incur high computacional cost. To cope with this limitation, the problem is explored by making use of an analytical methodology known as kernel-target alignment, where the kernel is optimized by aligning it to the so-called ideal kernel matrix. The results show that the proposal leads to better performance and simpler models at limited computational cost when applying the binary Support Vector Machine (SVM) paradigm.

关键词： Optimization

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Linear osmosis models for visual computing

Linear osmosis models for visual computing

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9th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2013

作者： Weickert, Joachim Hagenburg, Kai Breuß, Michael Vogel, Oliver Mathematical Image Analysis Group Dept. of Mathematics and Computer Science Saarland University 66041 Saarbrücken Germany Institute for Applied Mathematics and Scientific Computing Brandenburg University of Technology Platz der Deutschen Einheit 1 03046 Cottbus Germany

ISBN: (纸本)9783642403941

Osmosis is a transport phenomenon that is omnipresent in nature. It differs from diffusion by the fact that it allows nonconstant steady states. In our paper we lay the foundations of osmosis filtering for visual computing applications. We model filters with osmotic properties by means of linear drift-diffusion processes. They preserve the average grey value and the nonnegativity of the initial image. Most interestingly, however, we show how the nonconstant steady state of an osmosis evolution can be steered by its drift vector field. We interpret this behaviour as a data integration mechanism. In the integrable case, we characterise the steady state as a minimiser of a suitable energy functional. In the nonintegrable case, we can exploit osmosis as a framework to fuse incompatible data in a visually convincing way. Osmotic data fusion differs from gradient domain methods by its intrinsic invariance under multiplicative grey scale changes. The osmosis framework constitutes a novel class of methods that can be taylored to solve various problems in image processing, computer vision, and computer graphics. We demonstrate its versatility by proposing osmosis models for compact image respresentation, shadow removal, and seamless image cloning. © 2013 Springer-Verlag.

关键词： Osmosis

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