Machine learning and data mining in pattern recognition(英文原版进口)

2001-01-01  

Springer  

Petra Perner .  

361  

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The LNAI series reports state-of-the-art results in artificial intelligence re-search，development，and education，at a high level and in both printed and electronic form. Enjoying tight cooperation with the R＆D community，with numerous individuals，as well as with prestigious organizations and societies，LNAI has grown into the most comprehensive artificial intelligence research forum available. The scope of LNAI spans the whole range of artificial intelligence and intelli-gent information processing including interdisciplinary topics in a variety ofapplication fields. The type of material published traditionally includes proceedings（published in time for the respective conference） post-proceedings（consisting of thoroughly revised final full papers） research monographs（which may be based on PhD work）

Invited Paper Technology of Text Mining Evaluation of Clinical Relevance of Clinical Laboratory Investigations by Data MiningCase-Based Reasoning and Associative Memory Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications Are Case-Based Reasoning and Dissimilarity-Based Classification Two Sides of the Same Coin? FAM-Based Fuzzy Inference for Detecting Shot TransitionsRule Induction and Grammars Rule-Based Ensemble Solutions for Regression Learning XML Grammars First-Order Rule Induction for the Recognition of Morphological Patterns in Topographic MapsClustering and Conceptual Clustering Concepts Learning with Fuzzy Clustering and Relevance Feedback LC: A Conceptual Clustering AlgorithmData Mining on Signal, Images, Text and Temporal-Spatial Data Data Mining Approach Based on Information-Statistical Analysis Application to Temporal-Spatial Data A Hybrid Tool for Data Mining in Picture Archiving System Feature Selection for a Real-World Learning Task Automatic Identification of Diatoms Using Decision Forests Validation of Text Clustering Based on Document ContentsNonlinear Function Learning and Neural Net Based Learning Statistical and Neural Approaches for Estimating Parameters of a Speckle Model Based on the Nakagami Distribution How to Automate Neural Net Based Learning Nonlinear Function Learning and Classification Using Optimal Radial Basis Function NetworksLearning for Handwriting Recognition Local Learning Framework for Recognition of Lowercase Handwritten Characters Mirror Image Learning for Handwritten Numeral RecognitionStatistical and Evolutionary Learning Face Detection by Aggregated Bayesian Network Classifiers Towards Self-Exploring Discriminating Features……Content-Based Image Retrieval Author Index

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