rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Artificial Neural Networks – ICANN 2010
    sub title: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part II
  name:
    name part: Diamantaras, Konstantinos.
    role:
      role term: editor.
  name:
    name part: Duch, Wlodek.
    role:
      role term: editor.
  name:
    name part: Iliadis, Lazaros S.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 543 p. 217 illus. online resource.
  abstract: th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15–18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability “to learn” by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters.
  table of contents: Kernel Algorithms – Support Vector Machines -- Convergence Improvement of Active Set Training for Support Vector Regressors -- The Complex Gaussian Kernel LMS Algorithm -- Support Vector Machines-Kernel Algorithms for the Estimation of the Water Supply in Cyprus -- Faster Directions for Second Order SMO -- Almost Random Projection Machine with Margin Maximization and Kernel Features -- A New Tree Kernel Based on SOM-SD -- Kernel-Based Learning from Infinite Dimensional 2-Way Tensors -- Semi-supervised Facial Expressions Annotation Using Co-Training with Fast Probabilistic Tri-Class SVMs -- An Online Incremental Learning Support Vector Machine for Large-scale Data -- A Common Framework for the Convergence of the GSK, MDM and SMO Algorithms -- The Support Feature Machine for Classifying with the Least Number of Features -- Knowledge Engineering and Decision Making -- Hidden Markov Model for Human Decision Process in a Partially Observable Environment -- Representing, Learning and Extracting Temporal Knowledge from Neural Networks: A Case Study -- Recurrent ANN -- Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients -- Layered Motion Segmentation with a Competitive Recurrent Network -- Selection of Training Data for Locally Recurrent Neural Network -- A Statistical Appraoch to Image Reconstruction from Projections Problem Using Recurrent Neural Network -- A Computational System of Metaphor Generation with Evaluation Mechanism -- Recurrence Enhances the Spatial Encoding of Static Inputs in Reservoir Networks -- Action Classification in Soccer Videos with Long Short-Term Memory Recurrent Neural Networks -- Reinforcement Learning -- A Hebbian-Based Reinforcement Learning Framework for Spike-Timing-Dependent Synapses -- An Incremental Probabilistic Neural Network for Regression and Reinforcement Learning Tasks -- Using Reinforcement Learning to Guide the Development of Self-organised Feature Maps for Visual Orienting -- Exploring Continuo…
  note: edited by Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis.
  subject:
    topic: Computer science
  subject:
    topic: Computers
  subject:
    topic: Algorithms
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Computation by Abstract Devices
  subject:
    topic: Algorithm Analysis and Problem Complexity
  subject:
    topic: Pattern Recognition
  subject:
    topic: Information Systems Applications (incl. Internet)
  subject:
    topic: Image Processing and Computer Vision
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Computer Science, 6353
  identifier: 9783642158223
  identifier: http://dx.doi.org/10.1007/978-3-642-15822-3
  record info:
    record creation date: 100812
    record change date: 20151204185149.0
    record identifier: 978-3-642-15822-3