Machine learning and data mining in pattern recognition : 5th international conference, MLDM 2007, Leipzig, Germany, July 18-20, 2007 ; proceedings / Petra Perner (ed.).

By: (5th : MLDM (Conference) (5th : 2007 : Leipzig, Germany)
Contributor(s): Perner, Petra
Material type: TextTextSeries: SerienbezeichnungLNCS sublibrary: ; Lecture notes in computer science: 4571.; Lecture notes in computer science: Publisher: Berlin ; New York : Springer, ©2007Description: 1 online resource (xiv, 913 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9783540734994; 3540734996; 9783540734987; 3540734988Other title: MLDM 2007Subject(s): Pattern perception -- Congresses | Machine learning -- Congresses | Data mining -- Congresses | Image processing -- Congresses | Cluster analysis -- Congresses | Data mining | Image processing | Cluster analysis | Pattern perception | Machine learning | Informatique | Cluster analysis | Data mining | Image processing | Machine learning | Pattern perception | beeldverwerking | image processing | machine vision | wiskunde | mathematics | computerwetenschappen | computer sciences | kunstmatige intelligentie | artificial intelligence | datamining | data mining | databasebeheer | database management | logica | logic | patroonherkenning | pattern recognition | Information and Communication Technology (General) | Informatie- en communicatietechnologie (algemeen)Genre/Form: Electronic books. | Electronic books. | Conference papers and proceedings. Additional physical formats: Print version:: Machine learning and data mining in pattern recognition.DDC classification: 006.4 LOC classification: Q327 | .M56 2007ebOther classification: TP181-532 | TP391. 4-532 | SS 4800 | 004 | DAT 708f Online resources: Click here to access online
Contents:
Front Matter; On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers; Comparison of a Novel Combined ECOC Strategy with Different Multiclass Algorithms Together with Parameter Optimization Methods; Multi-source Data Modelling: Integrating Related Data to Improve Model Performance; An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules; Outlier Detection with Kernel Density Functions; Generic Probability Density Function Reconstruction for Randomization in Privacy-Preserving Data Mining.
An Incremental Fuzzy Decision Tree Classification Method for Mining Data StreamsOn the Combination of Locally Optimal Pairwise Classifiers; An Agent-Based Approach to the Multiple-Objective Selection of Reference Vectors; On Applying Dimension Reduction for Multi-labeled Problems; Nonlinear Feature Selection by Relevance Feature Vector Machine; Affine Feature Extraction: A Generalization of the Fukunaga-Koontz Transformation; A Bounded Index for Cluster Validity; Varying Density Spatial Clustering Based on a Hierarchical Tree; Kernel MDL to Determine the Number of Clusters.
Critical Scale for Unsupervised Cluster DiscoveryMinimum Information Loss Cluster Analysis for Categorical Data; A Clustering Algorithm Based on Generalized Stars; Evolving Committees of Support Vector Machines; Choosing the Kernel Parameters for the Directed Acyclic Graph Support Vector Machines; Data Selection Using SASH Trees for Support Vector Machines; Dynamic Distance-Based Active Learning with SVM; Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation; Transductive Learning from Relational Data.
A Novel Rule Ordering Approach in Classification Association Rule MiningDistributed and Shared Memory Algorithm for Parallel Mining of Association Rules; Analyzing the Performance of Spam Filtering Methods When Dimensionality of Input Vector Changes; Blog Mining for the Fortune 500; A Link-Based Rank of Postings in Newsgroup; A Comparative Study of Unsupervised Machine Learning and Data Mining Techniques for Intrusion Detection; Long Tail Attributes of Knowledge Worker Intranet Interactions; A Case-Based Approach to Anomaly Intrusion Detection.
Sensing Attacks in Computers Networks with Hidden Markov ModelsFIDS: Monitoring Frequent Items over Distributed Data Streams; Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree; CCIC: Consistent Common Itemsets Classifier; Development of an Agreement Metric Based Upon the RAND Index for the Evaluation of Dimensionality Reduction Techniques, with Applications to Mapping Customer Data; A Sequential Hybrid Forecasting System for Demand Prediction; A Unified View of Objective Interestingness Measures; Comparing State-of-the-Art Collaborative Filtering Systems.
Summary: This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007, held in Leipzig, Germany, in July 2007. The 66 revised full papers presented together with 1 invited talk were carefully reviewed and selected from 258 submissions. The papers are organized in topical sections on classification; feature selection, extraction and dimensionality reduction; clustering; support vector machines; transductive inference; association rule mining; mining spam, newsgroups, blogs; intrusion detection and networks; frequ.
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Includes bibliographical references and index.

Print version record.

Front Matter; On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers; Comparison of a Novel Combined ECOC Strategy with Different Multiclass Algorithms Together with Parameter Optimization Methods; Multi-source Data Modelling: Integrating Related Data to Improve Model Performance; An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules; Outlier Detection with Kernel Density Functions; Generic Probability Density Function Reconstruction for Randomization in Privacy-Preserving Data Mining.

An Incremental Fuzzy Decision Tree Classification Method for Mining Data StreamsOn the Combination of Locally Optimal Pairwise Classifiers; An Agent-Based Approach to the Multiple-Objective Selection of Reference Vectors; On Applying Dimension Reduction for Multi-labeled Problems; Nonlinear Feature Selection by Relevance Feature Vector Machine; Affine Feature Extraction: A Generalization of the Fukunaga-Koontz Transformation; A Bounded Index for Cluster Validity; Varying Density Spatial Clustering Based on a Hierarchical Tree; Kernel MDL to Determine the Number of Clusters.

Critical Scale for Unsupervised Cluster DiscoveryMinimum Information Loss Cluster Analysis for Categorical Data; A Clustering Algorithm Based on Generalized Stars; Evolving Committees of Support Vector Machines; Choosing the Kernel Parameters for the Directed Acyclic Graph Support Vector Machines; Data Selection Using SASH Trees for Support Vector Machines; Dynamic Distance-Based Active Learning with SVM; Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation; Transductive Learning from Relational Data.

A Novel Rule Ordering Approach in Classification Association Rule MiningDistributed and Shared Memory Algorithm for Parallel Mining of Association Rules; Analyzing the Performance of Spam Filtering Methods When Dimensionality of Input Vector Changes; Blog Mining for the Fortune 500; A Link-Based Rank of Postings in Newsgroup; A Comparative Study of Unsupervised Machine Learning and Data Mining Techniques for Intrusion Detection; Long Tail Attributes of Knowledge Worker Intranet Interactions; A Case-Based Approach to Anomaly Intrusion Detection.

Sensing Attacks in Computers Networks with Hidden Markov ModelsFIDS: Monitoring Frequent Items over Distributed Data Streams; Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree; CCIC: Consistent Common Itemsets Classifier; Development of an Agreement Metric Based Upon the RAND Index for the Evaluation of Dimensionality Reduction Techniques, with Applications to Mapping Customer Data; A Sequential Hybrid Forecasting System for Demand Prediction; A Unified View of Objective Interestingness Measures; Comparing State-of-the-Art Collaborative Filtering Systems.

This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007, held in Leipzig, Germany, in July 2007. The 66 revised full papers presented together with 1 invited talk were carefully reviewed and selected from 258 submissions. The papers are organized in topical sections on classification; feature selection, extraction and dimensionality reduction; clustering; support vector machines; transductive inference; association rule mining; mining spam, newsgroups, blogs; intrusion detection and networks; frequ.

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