Machine Learning and Data Mining in Pattern Recognition

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

Häftad, Engelska, 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.

Produktinformation

Utgivningsdatum2009-07-10
Mått155 x 235 x 45 mm
Vikt1 247 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Artificial Intelligence
Antal sidor824
Upplaga2009
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783642030697

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Tillämpad datateknik inom Data och it
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Attribute Discretization and Data Preparation.- Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization.- Selection of Subsets of Ordered Features in Machine Learning.- Combination of Vector Quantization and Visualization.- Discretization of Target Attributes for Subgroup Discovery.- Preserving Privacy in Time Series Data Classification by Discretization.- Using Resampling Techniques for Better Quality Discretization.- Classification.- A Large Margin Classifier with Additional Features.- Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier.- Optimal Double-Kernel Combination for Classification.- Efficient AdaBoost Region Classification.- A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation.- PMCRI: A Parallel Modular Classification Rule Induction Framework.- Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method.- ODDboost: Incorporating Posterior Estimates into AdaBoost.- Ensemble Classifier Learning.- Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach.- Relevance and Redundancy Analysis for Ensemble Classifiers.- Drift-Aware Ensemble Regression.- Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees.- Association Rules and Pattern Mining.- Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies.- Pattern Mining with Natural Language Processing: An Exploratory Approach.- Is the Distance Compression Effect Overstated? Some Theory and Experimentation.- Support Vector Machines.- Fast Local Support Vector Machines for Large Datasets.- The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines.- Towards B-Coloring of SOM.- Clustering.- CSBIterKmeans: A New Clustering Algorithm Based on Quantitative Assessment of the Clustering Quality.- Agent-Based Non-distributed and Distributed Clustering.- An Evidence Accumulation Approach to Constrained Clustering Combination.- Fast Spectral Clustering with Random Projection and Sampling.- How Much True Structure Has Been Discovered?.- Efficient Clustering of Web-Derived Data Sets.- A Probabilistic Approach for Constrained Clustering with Topological Map.- Novelty and Outlier Detection.- Relational Frequent Patterns Mining for Novelty Detection from Data Streams.- A Comparative Study of Outlier Detection Algorithms.- Outlier Detection with Explanation Facility.- Learning.- Concept Learning from (Very) Ambiguous Examples.- Finding Top-N Pseudo Formal Concepts with Core Intents.- On Fixed Convex Combinations of No-Regret Learners.- An Improved Tabu Search (ITS) Algorithm Based on Open Cover Theory for Global Extremums.- The Needles-in-Haystack Problem.- Data Mining on Multimedia Data.- An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding.- Detection of Masses in Mammographic Images Using Simpson’s Diversity Index in Circular Regions and SVM.- Mining Lung Shape from X-Ray Images.- A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data.- Spectrum Steganalysis of WAV Audio Streams.- Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach.- Learning with a Quadruped Chopstick Robot.- Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes.- Text Mining.- Using Graph-Kernels to Represent Semantic Information in Text Classification.- A General Framework of Feature Selection for Text Categorization.- New SemanticSimilarity Based Model for Text Clustering Using Extended Gloss Overlaps.- Aspects of Data Mining.- Learning Betting Tips from Users’ Bet Selections.- An Approach to Web-Scale Named-Entity Disambiguation.- A General Learning Method for Automatic Title Extraction from HTML Pages.- Regional Pattern Discovery in Geo-referenced Datasets Using PCA.- Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series.- A Neural Approach for SME’s Credit Risk Analysis in Turkey.- Assisting Data Mining through Automated Planning.- Predictions with Confidence in Applications.- Data Mining in Medicine.- Aligning Bayesian Network Classifiers with Medical Contexts.- Assessing the Eligibility of Kidney Transplant Donors.- Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM.