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  • Produktbild: Machine Learning and Data Mining in Pattern Recognition
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Band 2734

Machine Learning and Data Mining in Pattern Recognition Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings

Aus der Reihe Lecture Notes in Artificial Intelligence

49,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

25.06.2003

Abbildungen

XII, 444 p.

Herausgeber

Petra Perner + weitere

Verlag

Springer Berlin

Seitenzahl

444

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

686 g

Auflage

2003

Sprache

Englisch

ISBN

978-3-540-40504-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

25.06.2003

Abbildungen

XII, 444 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

444

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

686 g

Auflage

2003

Sprache

Englisch

ISBN

978-3-540-40504-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Machine Learning and Data Mining in Pattern Recognition
  • Produktbild: Machine Learning and Data Mining in Pattern Recognition
  • Invited Talkes.- Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers.- Graph-Based Tools for Data Mining and Machine Learning.- Decision Trees.- 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.- Clustering and Its Applications.- A Comparative Analysis of Clustering Algorithms Applied to Load Profiling.- Similarity-Based Clustering of Sequences Using Hidden Markov Models.- Support Vector Machines.- A Fast Parallel Optimization for Training Support Vector Machine.- A ROC-Based Reject Rule for Support Vector Machines.- Case-Based Reasoning.- Remembering Similitude Terms in CBR.- Authoring Cases from Free-Text Maintenance Data.- Classification, Retrieval, and Feature Learning.- 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.- Coevolutionary Feature Learning for Object Recognition.- Discovery of Frequently or Sequential Patterns.- 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.- Complexity Analysis of Depth First and FP-Growth Implementations of APRIORI.- Bayesian Models and Methods.- GO-SPADE: Mining Sequential Patterns over Datasets with Consecutive Repetitions.- Using Test Plans for Bayesian Modeling.- Using Bayesian Networks to Analyze Medical Data.- A Belief Networks-Based Generative Model for Structured Documents. An Application to the XML Categorization.- Neural Self-Organization Using Graphs.- Association Rules Mining.- Integrating Fuzziness with OLAP Association Rules Mining.- Discovering Association Patterns Based on Mutual Information.- Applications.- Connectionist Probability Estimators in HMM Arabic Speech Recognition Using Fuzzy Logic.- Shape Recovery from an Unorganized Image Sequence.- A Learning Autonomous Driver System on the Basis of Image Classification and Evolutional Learning.- Detecting the Boundary Curve of Planar Random Point Set.- A Machine Learning Model for Information Retrieval with Structured Documents.