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: Machine Learning and Data Mining in Pattern Recognition
    sub title: 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005. Proceedings
  name:
    name part: Perner, Petra.
    role:
      role term: editor.
  name:
    name part: Imiya, Atsushi.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 698 p. online resource.
  abstract: We met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.
  table of contents: Classification and Model Estimation -- On ECOC as Binary Ensemble Classifiers -- Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis -- Parameter Inference of Cost-Sensitive Boosting Algorithms -- Finite Mixture Models with Negative Components -- MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection -- Principles of Multi-kernel Data Mining -- Neural Methods -- Comparative Analysis of Genetic Algorithm, Simulated Annealing and Cutting Angle Method for Artificial Neural Networks -- Determining Regularization Parameters for Derivative Free Neural Learning -- A Comprehensible SOM-Based Scoring System -- Subspace Methods -- The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets -- SSC: Statistical Subspace Clustering -- Understanding Patterns with Different Subspace Classification -- Clustering: Basics -- Using Clustering to Learn Distance Functions for Supervised Similarity Assessment -- Linear Manifold Clustering -- Universal Clustering with Regularization in Probabilistic Space -- Acquisition of Concept Descriptions by Conceptual Clustering -- Applications of Clustering -- Clustering Large Dynamic Datasets Using Exemplar Points -- Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files -- Alarm Clustering for Intrusion Detection Systems in Computer Networks -- Clustering Document Images Using Graph Summaries -- Feature Grouping, Discretization, Selection and Transformation -- Feature Selection Method Using Preferences Aggregation -- Ranked Modelling with Feature Selection Based on the CPL Criterion Functions -- A Grouping Method for Categorical Attributes Having Very Large Number of Values -- Unsupervised Learning of Visual Feature Hierarchies -- Multivariate Discretization by Recursive Supervised Bipartition of Graph -- CorePhrase: Keyphrase Extraction for Document Clustering -- A New Multidimensional Feature Transformat…
  note: edited by Petra Perner, Atsushi Imiya.
  subject:
    topic: Computer science
  subject:
    topic: Mathematical logic
  subject:
    topic: Database management
  subject:
    topic: Information storage and retrieval
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Mathematical Logic and Formal Languages
  subject:
    topic: Database Management
  subject:
    topic: Information Storage and Retrieval
  subject:
    topic: Pattern Recognition
  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, 3587
  identifier: 9783540318910
  identifier: http://dx.doi.org/10.1007/b138149
  record info:
    record creation date: 100718
    record change date: 20151123194824.0
    record identifier: 978-3-540-31891-0