"Machine Learning and Data Mining in Pattern Recognition" - Information and Links:

Machine Learning and Data Mining in Pattern Recognition - Info and Reading Options

Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, Proceedings

"Machine Learning and Data Mining in Pattern Recognition" was published by Springer London, Limited in 2006 - Berlin/Heidelberg, it has 1 pages and the language of the book is English.


“Machine Learning and Data Mining in Pattern Recognition” Metadata:

  • Title: ➤  Machine Learning and Data Mining in Pattern Recognition
  • Authors:
  • Language: English
  • Number of Pages: 1
  • Publisher: Springer London, Limited
  • Publish Date:
  • Publish Location: Berlin/Heidelberg

“Machine Learning and Data Mining in Pattern Recognition” Subjects and Themes:

Edition Specifications:

  • Pagination: xii, 444

Edition Identifiers:

AI-generated Review of “Machine Learning and Data Mining in Pattern Recognition”:


"Machine Learning and Data Mining in Pattern Recognition" Description:

Open Data:

Lecture Notes in Artificial Intelligence -- Machine Learning and Data Mining in Pattern Recognition -- Copyright -- Preface -- Table of Contents -- Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers -- Graph-Based Tools for Data Mining and Machine Learning -- Simplification Methods for Model Trees with Regression and Splitting Nodes -- Learning Multi-label Alternating Decision Trees from Texts and Data★ -- Khiops: A Discretization Method of Continuous Attributes with Guaranteed Resistance to Noise -- On the Size of a Classification Tree -- A Comparative Analysis of Clustering Algorithms Applied to Load Profiling -- Similarity-Based Clustering of Sequences Using Hidden Markov Models -- A Fast Parallel Optimization for Training Support Vector Machine -- A ROC-Based Reject Rule for Support Vector Machines -- Remembering Similitude Terms in CBR -- Authoring Cases from Free-Text Maintenance Data -- Classification Boundary Approximation by Using Combination of Training Steps for Real-Time Image Segmentation -- Simple Mimetic Classifiers★ -- Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification -- Estimating a Quality of Decision Function by Empirical Risk -- Efficient Locally Linear Embeddings of Imperfect Manifolds -- Dissimilarity Representation of Images for Relevance Feedback in Content-Based Image Retrieval -- A Rule-Based Scheme for Filtering Examples from Majority Class in an Imbalanced Training Set -- Generalization of Pattern-Growth Methods for Sequential Pattern Mining with Gap Constraints -- Discover Motifs in Multi-dimensional Time-Series Using the Principal Component Analysis and the MDL Principle -- Optimizing Financial Portfolios from the Perspective of Mining Temporal Structures of Stock Returns -- Visualizing Sequences of Texts Using Collocational Networks

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