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  • Produktbild: Machine Learning and Data Mining in Pattern Recognition
  • Produktbild: Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition 6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings

Aus der Reihe Lecture Notes in Artificial Intelligence

97,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.2009

Herausgeber

Petra Perner

Verlag

Springer Berlin

Seitenzahl

824

Maße (L/B/H)

23,5/15,5/4,5 cm

Gewicht

1247 g

Auflage

2009

Sprache

Englisch

ISBN

978-3-642-03069-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.2009

Herausgeber

Petra Perner

Verlag

Springer Berlin

Seitenzahl

824

Maße (L/B/H)

23,5/15,5/4,5 cm

Gewicht

1247 g

Auflage

2009

Sprache

Englisch

ISBN

978-3-642-03069-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Machine Learning and Data Mining in Pattern Recognition
  • Produktbild: Machine Learning and Data Mining in Pattern Recognition
  • 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 Semantic Similarity 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.