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Machine Learning and Data Mining in Pattern Recognition

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Cover of 'Machine Learning and Data Mining in Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization
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    Chapter 2 Selection of Subsets of Ordered Features in Machine Learning
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    Chapter 3 Combination of Vector Quantization and Visualization
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    Chapter 4 Discretization of Target Attributes for Subgroup Discovery
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    Chapter 5 Preserving Privacy in Time Series Data Classification by Discretization
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    Chapter 6 Using Resampling Techniques for Better Quality Discretization
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    Chapter 7 A Large Margin Classifier with Additional Features
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    Chapter 8 Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier
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    Chapter 9 Optimal Double-Kernel Combination for Classification
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    Chapter 10 Efficient AdaBoost Region Classification
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    Chapter 11 A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation
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    Chapter 12 PMCRI: A Parallel Modular Classification Rule Induction Framework
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    Chapter 13 Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method
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    Chapter 14 ODDboost: Incorporating Posterior Estimates into AdaBoost
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    Chapter 15 Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach
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    Chapter 16 Relevance and Redundancy Analysis for Ensemble Classifiers
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    Chapter 17 Drift-Aware Ensemble Regression
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    Chapter 18 Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
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    Chapter 19 Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies
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    Chapter 20 Pattern Mining with Natural Language Processing: An Exploratory Approach
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    Chapter 21 Is the Distance Compression Effect Overstated? Some Theory and Experimentation
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    Chapter 22 Fast Local Support Vector Machines for Large Datasets
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    Chapter 23 The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines
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    Chapter 24 Towards B-Coloring of SOM
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    Chapter 25 CSBIterKmeans: A New Clustering Algorithm Based on Quantitative Assessment of the Clustering Quality
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    Chapter 26 Agent-Based Non-distributed and Distributed Clustering
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    Chapter 27 An Evidence Accumulation Approach to Constrained Clustering Combination
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    Chapter 28 Fast Spectral Clustering with Random Projection and Sampling
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    Chapter 29 How Much True Structure Has Been Discovered?
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    Chapter 30 Efficient Clustering of Web-Derived Data Sets
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    Chapter 31 A Probabilistic Approach for Constrained Clustering with Topological Map
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    Chapter 32 Relational Frequent Patterns Mining for Novelty Detection from Data Streams
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    Chapter 33 A Comparative Study of Outlier Detection Algorithms
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    Chapter 34 Outlier Detection with Explanation Facility
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    Chapter 35 Concept Learning from (Very) Ambiguous Examples
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    Chapter 36 Finding Top- N Pseudo Formal Concepts with Core Intents
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    Chapter 37 On Fixed Convex Combinations of No-Regret Learners
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    Chapter 38 An Improved Tabu Search (ITS) Algorithm Based on Open Cover Theory for Global Extremums
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    Chapter 39 The Needles-in-Haystack Problem
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    Chapter 40 An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding
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    Chapter 41 Detection of Masses in Mammographic Images Using Simpson’s Diversity Index in Circular Regions and SVM
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    Chapter 42 Mining Lung Shape from X-Ray Images
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    Chapter 43 A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data
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    Chapter 44 Spectrum Steganalysis of WAV Audio Streams
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    Chapter 45 Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach
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    Chapter 46 Learning with a Quadruped Chopstick Robot
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    Chapter 47 Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes
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    Chapter 48 Using Graph-Kernels to Represent Semantic Information in Text Classification
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    Chapter 49 A General Framework of Feature Selection for Text Categorization
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    Chapter 50 New Semantic Similarity Based Model for Text Clustering Using Extended Gloss Overlaps
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    Chapter 51 Learning Betting Tips from Users’ Bet Selections
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    Chapter 52 An Approach to Web-Scale Named-Entity Disambiguation
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    Chapter 53 A General Learning Method for Automatic Title Extraction from HTML Pages
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    Chapter 54 Regional Pattern Discovery in Geo-referenced Datasets Using PCA
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    Chapter 55 Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series
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    Chapter 56 A Neural Approach for SME’s Credit Risk Analysis in Turkey
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    Chapter 57 Assisting Data Mining through Automated Planning
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    Chapter 58 Predictions with Confidence in Applications
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    Chapter 59 Aligning Bayesian Network Classifiers with Medical Contexts
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    Chapter 60 Assessing the Eligibility of Kidney Transplant Donors
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    Chapter 61 Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM
Attention for Chapter 12: PMCRI: A Parallel Modular Classification Rule Induction Framework
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Chapter title
PMCRI: A Parallel Modular Classification Rule Induction Framework
Chapter number 12
Book title
Machine Learning and Data Mining in Pattern Recognition
Published in
Lecture notes in computer science, January 2009
DOI 10.1007/978-3-642-03070-3_12
Book ISBNs
978-3-64-203069-7, 978-3-64-203070-3
Authors

Stahl, Frederic, Bramer, Max, Adda, Mo, Frederic Stahl, Max Bramer, Mo Adda

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 20%
Student > Ph. D. Student 1 20%
Researcher 1 20%
Student > Master 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 3 60%
Engineering 1 20%
Unknown 1 20%