Pub. Date:
Springer Berlin Heidelberg
Machine Learning and Data Mining in Pattern Recognition: 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings / Edition 1

Machine Learning and Data Mining in Pattern Recognition: 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings / Edition 1

by Petra Perner, Atsushi Imiya


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Product Details

ISBN-13: 9783540269236
Publisher: Springer Berlin Heidelberg
Publication date: 08/22/2005
Series: Lecture Notes in Computer Science , #3587
Edition description: 2005
Pages: 698
Product dimensions: 6.10(w) x 9.25(h) x 0.06(d)

Table of Contents

Classification and Model Estimation.- On ECOC as Binary Ensemble Classifiers.- Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis.- Parameter Inference of Cost-Sensitive Boosting Algorithms.- Finite Mixture Models with Negative Components.- MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection.- Principles of Multi-kernel Data Mining.- Neural Methods.- Comparative Analysis of Genetic Algorithm, Simulated Annealing and Cutting Angle Method for Artificial Neural Networks.- Determining Regularization Parameters for Derivative Free Neural Learning.- A Comprehensible SOM-Based Scoring System.- Subspace Methods.- The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets.- SSC: Statistical Subspace Clustering.- Understanding Patterns with Different Subspace Classification.- Clustering: Basics.- Using Clustering to Learn Distance Functions for Supervised Similarity Assessment.- Linear Manifold Clustering.- Universal Clustering with Regularization in Probabilistic Space.- Acquisition of Concept Descriptions by Conceptual Clustering.- Applications of Clustering.- Clustering Large Dynamic Datasets Using Exemplar Points.- Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files.- Alarm Clustering for Intrusion Detection Systems in Computer Networks.- Clustering Document Images Using Graph Summaries.- Feature Grouping, Discretization, Selection and Transformation.- Feature Selection Method Using Preferences Aggregation.- Ranked Modelling with Feature Selection Based on the CPL Criterion Functions.- A Grouping Method for Categorical Attributes Having Very Large Number of Values.- Unsupervised Learning of Visual Feature Hierarchies.- Multivariate Discretization by Recursive Supervised Bipartition of Graph.- CorePhrase: Keyphrase Extraction for Document Clustering.- A New Multidimensional Feature Transformation for Linear Classifiers and Its Applications.- Applications in Medicine.- Comparison of FLDA, MLP and SVM in Diagnosis of Lung Nodule.- Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures.- Iris Recognition Algorithm Based on Point Covering of High-Dimensional Space and Neural Network.- Automatic Clinical Image Segmentation Using Pathological Modelling, PCA and SVM.- Improved MRI Mining by Integrating Support Vector Machine Priors in the Bayesian Restoration.- Prediction of Secondary Protein Structure Content from Primary Sequence Alone – A Feature Selection Based Approach.- Alternative Clustering by Utilizing Multi-objective Genetic Algorithm with Linked-List Based Chromosome Encoding.- Time Series and Sequential Pattern Mining.- Embedding Time Series Data for Classification.- Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning.- Disjunctive Sequential Patterns on Single Data Sequence and Its Anti-monotonicity.- Mining Expressive Temporal Associations from Complex Data.- Statistical Supports for Frequent Itemsets on Data Streams.- Mining Images in Computer Vision.- Autonomous Vehicle Steering Based on Evaluative Feedback by Reinforcement Learning.- Cost Integration in Multi-step Viewpoint Selection for Object Recognition.- Support Vector Machine Experiments for Road Recognition in High Resolution Images.- An Automatic Face Recognition System in the Near Infrared Spectrum.- Mining Images and Texture.- Hierarchical Partitions for Content Image Retrieval from Large-Scale Database.- Optimising the Choice of Colours of an Image Database for Dichromats.- An Approach to Mining Picture Objects Based on Textual Cues.- Mining Motion from Sequence.- Activity and Motion Detection Based on Measuring Texture Change.- A New Approach to Human Motion Sequence Recognition with Application to Diving Actions.- Dominant Plane Detection Using Optical Flow and Independent Component Analysis.- Speech Analysis.- Neural Expert Model Applied to Phonemes Recognition.- An Evidential Reasoning Approach to Weighted Combination of Classifiers for Word Sense Disambiguation.- Aspects of Data Mining.- Signature-Based Approach for Intrusion Detection.- Discovery of Hidden Correlations in a Local Transaction Database Based on Differences of Correlations.- An Integrated Approach for Mining Meta-rules.- Data Mining on Crash Simulation Data.- Text Mining.- Pattern Mining Across Domain-Specific Text Collections.- Text Classification Using Small Number of Features.- Low-Level Cursive Word Representation Based on Geometric Decomposition.- Special Track: Industrial Applications of Data Mining.- Supervised Evaluation of Dataset Partitions: Advantages and Practice.- Inference on Distributed Data Clustering.- A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases.- Mixture Random Effect Model Based Meta-analysis for Medical Data Mining.- Semantic Analysis of Association Rules via Item Response Theory.- Temporal Approach to Association Rule Mining Using T-Tree and P-Tree.- Aquaculture Feature Extraction from Satellite Image Using Independent Component Analysis.- Modeling the Organoleptic Properties of Matured Wine Distillates.- Bagging Random Trees for Estimation of Tissue Softness.- Concept Mining for Indexing Medical Literature.

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