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Artificial neural networks and machine learning-- ICANN 2012 [electronic resource] : 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings. Part I /

Alessandro E. Villa ... [et al.] (eds.).

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Names: Villa, Alessandro E. P.
Published: Berlin ; Springer, c2012.
Series: Lecture notes in computer science ; 7552.
LNCS sublibrary. SL 1, Theoretical computer science and general issues.
Topics: Neural networks (Computer science) - Congresses. | Machine learning - Congresses.
Genres: Conference proceedings. | Electronic books.
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020 |a9783642332692 (electronic bk.)
020 |a3642332692 (electronic bk.)
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035 |a(OCoLC)ocn811620802
035 |a(CSUdb)681599
049 |aIAAA
050 4|aQA76.87|b.I23 2012
082 04|a006.3/2|223
111 2 |aICANN 2012|d(2012 :|cLausanne, Switzerland)
245 10|aArtificial neural networks and machine learning-- ICANN 2012|h[electronic resource] :|b22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings.|nPart I /|cAlessandro E. Villa ... [et al.] (eds.).
246 3 |aICANN 2012
260 |aBerlin ;|aNew York :|bSpringer,|cc2012.
300 |a1 online resource.
490 1 |aLecture notes in computer science,|x0302-9743 ;|v7552
490 1 |aLNCS sublibrary. SL 1, Theoretical computer science and general issues
500 |aInternational conference proceedings.
504 |aIncludes bibliographical references and author index.
505 00|tTemporal Patterns in Artificial Reaction Networks /|rClaire Gerrard, John McCall, George M. Coghill and Christopher Macleod --|tProperties of the Hopfield Model with Weighted Patterns /|rIakov Karandashev, Boris Kryzhanovsky and Leonid Litinskii --|tDynamics and Oscillations of GHNNs with Time-Varying Delay /|rFarouk Chérif --|tA Dynamic Field Architecture for the Generation of Hierarchically Organized Sequences /|rBoris Durán, Yulia Sandamirskaya and Gregor Schöner --|tStochastic Techniques in Influence Diagrams for Learning Bayesian Network Structure /|rMichal Matuszak and Jacek Miękisz --|tThe Mix-Matrix Method in the Problem of Binary Quadratic Optimization /|rIakov Karandashev and Boris Kryzhanovsky --|tA Rule Chaining Architecture Using a Correlation Matrix Memory /|rJames Austin, Stephen Hobson, Nathan Burles and Simon O'Keefe --|tA Generative Multiset Kernel for Structured Data /|rDavide Bacciu, Alessio Micheli and Alessandro Sperduti --|tSpectral Signal Unmixing with Interior-Point Nonnegative Matrix Factorization /|rRafal Zdunek --|tHybrid Optimized Polynomial Neural Networks with Polynomial Neurons and Fuzzy Polynomial Neurons /|rDan Wang, Donghong Ji and Wei Huang --
505 00|tTikhonov-Type Regularization for Restricted Boltzmann Machines /|rKyungHyun Cho, Alexander Ilin and Tapani Raiko --|tModeling of Spiking Analog Neural Circuits with Hebbian Learning, Using Amorphous Semiconductor Thin Film Transistors with Silicon Oxide Nitride Semiconductor Split Gates /|rRichard Wood, Ian Bruce and Peter Mascher --|tReal-Time Simulations of Synchronization in a Conductance-Based Neuronal Network with a Digital FPGA Hardware-Core /|rMarcel Beuler, Aubin Tchaptchet, Werner Bonath, Svetlana Postnova and Hans Albert Braun --|tImpact of Frequency on the Energetic Efficiency of Action Potentials /|rAnand Singh, Pierre J. Magistretti, Bruno Weber and Renaud Jolivet --|tA Large-Scale Spiking Neural Network Accelerator for FPGA Systems /|rKit Cheung, Simon R. Schultz and Wayne Luk --|tSilicon Neurons That Compute /|rSwadesh Choudhary, Steven Sloan, Sam Fok, Alexander Neckar and Eric Trautmann, et al. --|tA Communication Infrastructure for Emulating Large-Scale Neural Networks Models /|rAndres Gaona Barrera and Manuel Moreno Arostegui --|tPair-Associate Learning with Modulated Spike-Time Dependent Plasticity /|rNooraini Yusoff, André Grüning and Scott Notley --
505 00|tAssociative Memory in Neuronal Networks of Spiking Neurons: Architecture and Storage Analysis /|rEverton J. Agnes, Rubem Erichsen Jr. and Leonardo G. Brunnet --|tBifurcating Neurons with Filtered Base Signals /|rShota Kirikawa, Takashi Ogawa and Toshimichi Saito --|tBasic Analysis of Digital Spike Maps /|rNarutoshi Horimoto, Takashi Ogawa and Toshimichi Saito --|tCyfield-RISP: Generating Dynamic Instruction Set Processors for Reconfigurable Hardware Using OpenCL /|rJörn Hoffmann, Frank Güttler, Karim El-Laithy and Martin Bogdan --|tA Biophysical Network Model Displaying the Role of Basal Ganglia Pathways in Action Selection /|rCem Yucelgen, Berat Denizdurduran, Selin Metin, Rahmi Elibol and Neslihan Serap Sengor --|tHow Degrading Networks Can Increase Cognitive Functions /|rAdam Tomkins, Mark Humphries, Christian Beste, Eleni Vasilaki and Kevin Gurney --|tEmergence of Connectivity Patterns from Long-Term and Short-Term Plasticities /|rEleni Vasilaki and Michele Giugliano --|tArtificial Neural Networks and Data Compression Statistics for the Discrimination of Cultured Neuronal Activity /|rAndres Perez-Uribe and Héctor F. Satizábal --
505 00|tLiquid Computing in a Simplified Model of Cortical Layer IV: Learning to Balance a Ball /|rDimitri Probst, Wolfgang Maass, Henry Markram and Marc-Oliver Gewaltig --|tTiming Self-generated Actions for Sensory Streaming /|rAngel A. Caputi --|tThe Capacity and the Versatility of the Pulse Coupled Neural Network in the Image Matching /|rYuta Ishida, Masato Yonekawa and Hiroaki Kurokawa --|tA Novel Bifurcation-Based Synthesis of Asynchronous Cellular Automaton Based Neuron /|rTakashi Matsubara and Hiroyuki Torikai --|tBiomimetic Binaural Sound Source Localisation with Ego-Noise Cancellation /|rJorge Dávila-Chacón, Stefan Heinrich, Jindong Liu and Stefan Wermter --|tA Biologically Realizable Bayesian Computation in a Cortical Neural Network /|rDaiki Futagi and Katsunori Kitano --|tEvaluating the Effect of Spiking Network Parameters on Polychronization /|rPanagiotis Ioannou, Matthew Casey and André Grüning --|tClassification of Distorted Patterns by Feed-Forward Spiking Neural Networks /|rIoana Sporea and André Grüning --|tSpike Transmission on Diverging/Converging Neural Network and Its Implementation on a Multilevel Modeling Platform /|rYoshiyuki Asai and Alessandro E. P. Villa --
505 00|tDifferential Entropy of Multivariate Neural Spike Trains /|rNanyi Cui, Jiaying Tang and Simon R. Schultz --|tLearning Representations for Animated Motion Sequence and Implied Motion Recognition /|rGeorg Layher, Martin A. Giese and Heiko Neumann --|tExploratory Behaviour Depends on Multisensory Integration during Spatial Learning /|rDenis Sheynikhovich, Félix Grèzes, Jean-Rémi King and Angelo Arleo --|tControl of Biped Robot Joints' Angles Using Coordinated Matsuoka Oscillators /|rAsiya M. Al-Busaidi, Riadh Zaier and Amer S. Al-Yahmadi --|tSelf-calibrating Marker Tracking in 3D with Event-Based Vision Sensors /|rGeorg R. Müller and Jörg Conradt --|tIntegration of Static and Self-motion-Based Depth Cues for Efficient Reaching and Locomotor Actions /|rBeata J. Grzyb, Vicente Castelló, Marco Antonelli and Angel P. del Pobil --|tA Proposed Neural Control for the Trajectory Tracking of a Nonholonomic Mobile Robot with Disturbances /|rNardênio A. Martins, Maycol de Alencar, Warody C. Lombardi, Douglas W. Bertol and Edson R. De Pieri, et al. --|tSimulating Light Adaptation in the Retina with Rod-Cone Coupling /|rKendi Muchungi and Matthew Casey --
505 00|tEvolving Neural Networks for Orientation Behavior of Sand Scorpions towards Prey /|rHyungu Yim and DaeEun Kim --|tEvolving Dendritic Morphology and Parameters in Biologically Realistic Model Neurons for Pattern Recognition /|rGiseli de Sousa, Reinoud Maex, Rod Adams, Neil Davey and Volker Steuber --|tNeural Network Providing Integrative Perception of Features and Subsecond Temporal Parameters of Sensory Stimuli /|rIsabella Silks --|tAn Effect of Short and Long Reciprocal Projections on Evolution of Hierarchical Neural Networks /|rVladyslav Shaposhnyk and Alessandro E. P. Villa --|tSome Things Psychopathologies Can Tell Us about Consciousness /|rRoseli S. Wedemann and Luís Alfredo V. de Carvalho --|tElastic Graph Matching on Gabor Feature Representation at Low Image Resolution /|rYasuomi D. Sato and Yasutaka Kuriya --|tContour Detection by CORF Operator /|rGeorge Azzopardi and Nicolai Petkov --|tHybrid Ensembles Using Hopfield Neural Networks and Haar-Like Features for Face Detection /|rNils Meins, Stefan Wermter and Cornelius Weber --|tFace Recognition with Disparity Corrected Gabor Phase Differences /|rManuel Günther, Dennis Haufe and Rolf P. Würtz --
505 00|tVisual Categorization Based on Learning Contextual Probabilistic Latent Component Tree /|rMasayasu Atsumi --|tBiological Brain and Binary Code: Quality of Coding for Face Recognition /|rJoão da Silva Gomes and Roman Borisyuk --|tMaking a Reinforcement Learning Agent Believe /|rKlaus Häming and Gabriele Peters --|tBiologically Plausible Multi-dimensional Reinforcement Learning in Neural Networks /|rJaldert O. Rombouts, Arjen van Ooyen, Pieter R. Roelfsema and Sander M. Bohte --|tAdaptive Neural Oscillator with Synaptic Plasticity Enabling Fast Resonance Tuning /|rTimo Nachstedt, Florentin Wörgötter and Poramate Manoonpong --|tLearning from Delayed Reward und Punishment in a Spiking Neural Network Model of Basal Ganglia with Opposing D1/D2 Plasticity /|rJenia Jitsev, Nobi Abraham, Abigail Morrison and Marc Tittgemeyer --|tUnderstanding the Role of Serotonin in Basal Ganglia through a Unified Model /|rBalasubramani Pragathi Priyadharsini, Balaraman Ravindran and V. Srinivasa Chakravarthy --|tLearning How to Select an Action: A Computational Model /|rBerat Denizdurduran and Neslihan Serap Sengor --|tA Dynamic Binding Mechanism for Retrieving and Unifying Complex Predicate-Logic Knowledge /|rGadi Pinkas, Priscila Lima and Shimon Cohen --
505 00|tEstimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders /|rTatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen and Takashi Washio --|tReservoir Sizes and Feedback Weights Interact Non-linearly in Echo State Networks /|rDanil Koryakin and Martin V. Butz --|tLearning to Imitate YMCA with an ESN /|rRikke Amilde Løvlid --|tA New Neural Data Analysis Approach Using Ensemble Neural Network Rule Extraction /|rAtsushi Hara and Yoichi Hayashi --|tBayesian Inference with Efficient Neural Population Codes /|rXue-Xin Wei and Alan A. Stocker --|tLearning Sequence Neighbourhood Metrics /|rJustin Bayer, Christian Osendorfer and Patrick van der Smagt --|tLearning Features and Predictive Transformation Encoding Based on a Horizontal Product Model /|rJunpei Zhong, Cornelius Weber and Stefan Wermter --|tRegulation toward Self-organized Criticality in a Recurrent Spiking Neural Reservoir /|rSimon Brodeur and Jean Rouat --|tAdaptive Learning of Linguistic Hierarchy in a Multiple Timescale Recurrent Neural Network /|rStefan Heinrich, Cornelius Weber and Stefan Wermter --|tThe Spherical Hidden Markov Self Organizing Map for Learning Time Series Data /|rGen Niina and Hiroshi Dozono --
505 00|tEcho State Networks for Multi-dimensional Data Clustering /|rPetia Koprinkova-Hristova and Nikolay Tontchev --|tThe Counter-Change Model of Motion Perception: An Account Based on Dynamic Field Theory /|rMichael Berger, Christian Faubel, Joseph Norman, Howard Hock and Gregor Schöner --|tSelf-organized Reservoirs and Their Hierarchies /|rMantas Lukoševičius --|tOn-Line Processing of Grammatical Structure Using Reservoir Computing /|rXavier Hinaut and Peter F. Dominey --|tConstructing Robust Liquid State Machines to Process Highly Variable Data Streams /|rStefan Schliebs, Maurizio Fiasché and Nikola Kasabov --|tInfinite Sparse Threshold Unit Networks /|rMichiel Hermans and Benjamin Schrauwen --|tLearning Two-Layer Contractive Encodings /|rHannes Schulz and Sven Behnke --|tEffects of Architecture Choices on Sparse Coding in Speech Recognition /|rFionntán O'Donnell, Fabian Triefenbach, Jean-Pierre Martens and Benjamin Schrauwen --|tGenerating Motion Trajectories by Sparse Activation of Learned Motion Primitives /|rChristian Vollmer, Julian P. Eggert and Horst-Michael Gro€ --|tKinetic Modelling of Synaptic Functions in the Alpha Rhythm Neural Mass Model /|rBasabdatta Sen Bhattacharya, Damien Coyle, Liam P. Maguire and Jill Stewart --
505 00|tIntegrating Neural Networks and Chaotic Measurements for Modelling Epileptic Brain /|rMaurizio Fiasché, Stefan Schliebs and Lino Nobili --|tDynamic Stopping Improves the Speed and Accuracy of a P300 Speller /|rHannes Verschore, Pieter-Jan Kindermans, David Verstraeten and Benjamin Schrauwen --|tAdaptive SVM-Based Classification Increases Performance of a MEG-Based Brain-Computer Interface (BCI) /|rMartin Spüler, Wolfgang Rosenstiel and Martin Bogdan --|tRecognizing Human Activities Using a Layered Markov Architecture /|rMichael Glodek, Georg Layher, Friedhelm Schwenker and Günther Palm --|tPSO for Reservoir Computing Optimization /|rAnderson Tenório Sergio and Teresa B. Ludermir --|tOne-Class Classification through Optimized Feature Boundaries Detection and Prototype Reduction /|rGeorge G. Cabral and Adriano L. I. Oliveira --|tBi-objective Genetic Algorithm for Feature Selection in Ensemble Systems /|rLaura E. A. Santana and Anne M. P. Canuto --|tDual Support Vector Domain Description for Imbalanced Classification /|rFelipe Ramírez and Héctor Allende --|tLearning Method Inspired on Swarm Intelligence for Fuzzy Cognitive Maps: Travel Behaviour Modelling /|rMaikel León, Lusine Mkrtchyan, Benoît Depaire, Da Ruan and Rafael Bello, et al. --|tA Computational Model of Motor Areas Based on Bayesian Networks and Most Probable Explanations /|rYuuji Ichisugi.
506 1 |aAccess restricted to current Chicago State University students, faculty and staff.
520 |aThe two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.
650 0|aNeural networks (Computer science)|vCongresses.
650 0|aMachine learning|vCongresses.
650 7|aMachine learning.|2fast|0(OCoLC)fst01004795
650 7|aNeural networks (Computer science)|2fast|0(OCoLC)fst01036260
653 4|aComputer science.
653 4|aComputer software.
653 4|aArtificial intelligence.
653 4|aComputer vision.
653 4|aOptical pattern recognition.
653 4|aComputation by Abstract Devices.
653 4|aAlgorithm Analysis and Problem Complexity.
653 4|aImage Processing and Computer Vision.
655 7|aConference proceedings.|2fast|0(OCoLC)fst01423772
655 0|aElectronic books.
700 1 |aVilla, Alessandro E. P.|4edt
830 0|aLecture notes in computer science ;|v7552.
830 0|aLNCS sublibrary.|nSL 1,|pTheoretical computer science and general issues.
852 0 |bebook|kCRLICS|hQA76.87|i.I23 2012eb|t1
856 40|3SpringerLink Computer Science (ebooks)|u|zavailable only to CSU students, faculty and staff. Click here to access.
951 2 |aSpringerLink (Online service)
953 2 |aSpringerLink ebooks.|gComputer Science.|f2012.
959 |a(CSUdb)681599

Staff View for: Artificial neural networks and machine l