Machine Learning and Data Mining in Pattern Recognition

6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings

By: Petra Perner (Editor)

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Paperback | 10 July 2009

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There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year's program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

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Attribute Discretization and Data Preparation
Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization	p. 1
Selection of Subsets of Ordered Features in Machine Learning	p. 16
Combination of Vector Quantization and Visualization	p. 29
Discretization of Target Attributes for Subgroup Discovery	p. 44
Preserving Privacy in Time Series Data Classification by Discretization	p. 53
Using Resampling Techniques for Better Quality Discretization	p. 68
Classification
A Large Margin Classifier with Additional Features	p. 82
Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier	p. 96
Optimal Double-Kernel Combination for Classification	p. 107
Efficient AdaBoost Region Classification	p. 123
A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation	p. 137
PMCRI: A Parallel Modular Classification Rule Induction Framework	p. 148
Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method	p. 163
ODDboost: Incorporating Posterior Estimates into AdaBoost	p. 178
Ensemble Classifier Learning
Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach	p. 191
Relevance and Redundancy Analysis for Ensemble Classifiers	p. 206
Drift-A ware Ensemble Regression	p. 221
Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees	p. 236
Association Rules and Pattern Mining
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies	p. 251
Pattern Mining with Natural Language Processing: An Exploratory Approach	p. 266
Is the Distance Compression Effect Overstated? Some Theory and Experimentation	p. 280
Support Vector Machines
Fast Local Support Vector Machines for Large Datasets	p. 295
The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines	p. 311
Towards B-Coloring of SOM	p. 322
Clustering
CSBIterKmeans: A New Clustering Algorithm Based on Quantitative Assessment of the Clustering Quality	p. 337
Agent-Based Non-distributed and Distributed Clustering	p. 347
An Evidence Accumulation Approach to Constrained Clustering Combination	p. 361
Fast Spectral Clustering with Random Projection and Sampling	p. 372
How Much True Structure Has Been Discovered?: Validating Explorative Clustering on a Hold-Out Test Set	p. 385
Efficient Clustering of Web-Derived Data Sets	p. 398
A Probabilistic Approach for Constrained Clustering with Topological Map	p. 413
Novelty and Outlier Detection
Relational Frequent Patterns Mining for Novelty Detection from Data Streams	p. 427
A Comparative Study of Outlier Detection Algorithms	p. 440
Outlier Detection with Explanation Facility	p. 454
Learning
Concept Learning from (Very) Ambiguous Examples	p. 465
Finding Top-TV Pseudo Formal Concepts with Core Intents	p. 479
On Fixed Convex Combinations of No-Regret Learners	p. 494
An Improved Tabu Search (ITS) Algorithm Based on Open Cover Theory for Global Extremums	p. 505
The Needles-in-Haystack Problem	p. 516
Data Mining on Multimedia Data
An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding	p. 525
Detection of Masses in Mammographic Images Using SimpsonÆs Diversity Index in Circular Regions and SVM	p. 540
Mining Lung Shape from X-Ray Images	p. 554
A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data	p. 569
Spectrum Steganalysis of WAV Audio Streams	p. 582
Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach	p. 594
Learning with a Quadruped Chopstick Robot	p. 603
Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes	p. 617
Text Mining
Using Graph-Kernels to Represent Semantic Information in Text Classification	p. 632
A General Framework of Feature Selection for Text Categorization	p. 647
New Semantic Similarity Based Model for Text Clustering Using Extended Gloss Overlaps	p. 663
Aspects of Data Mining
Learning Betting Tips from UsersÆ Bet Selections	p. 678
An Approach to Web-Scale Named-Entity Disambiguation	p. 689
A General Learning Method for Automatic Title Extraction from HTML Pages	p. 704
Regional Pattern Discovery in Geo-referenced Datasets Using PCA	p. 719
Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series	p. 734
A Neural Approach for SMEÆs Credit Risk Analysis in Turkey	p. 749
Assisting Data Mining through Automated Planning	p. 760
Predictions with Confidence in Applications	p. 775
Data Mining in Medicine
Aligning Bayesian Network Classifiers with Medical Contexts	p. 787
Assessing the Eligibility of Kidney Transplant Donors	p. 802
Lung Nodules Classification in CT Images Using SimpsonÆs Index, Geometrical Measures and One-Class SVM	p. 810
Author Index	p. 823
Table of Contents provided by Ingram. All Rights Reserved.

Machine Learning and Data Mining in Pattern Recognition

6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings

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