Machine Learning and Data Mining in Pattern Recognition - Perner, Petra / Rosenfeld, Azriel (Hgg.)
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TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for…mehr

Produktbeschreibung
TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.
  • Produktdetails
  • Lecture Notes in Computer Science 2734
  • Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
  • Artikelnr. des Verlages: 978-3-540-40504-7
  • 2003
  • Seitenzahl: 456
  • Erscheinungstermin: 25. Juni 2003
  • Englisch
  • Abmessung: 235mm x 155mm x 24mm
  • Gewicht: 640g
  • ISBN-13: 9783540405047
  • ISBN-10: 3540405046
  • Artikelnr.: 21169207
Inhaltsangabe
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.