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

作者简介

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）

书籍目录

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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