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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

  1. Altmetric Badge
    Book Overview
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    Chapter 1 On ECOC as Binary Ensemble Classifiers
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    Chapter 2 Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis
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    Chapter 3 Parameter Inference of Cost-Sensitive Boosting Algorithms
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    Chapter 4 Finite Mixture Models with Negative Components
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    Chapter 5 MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection
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    Chapter 6 Principles of Multi-kernel Data Mining
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    Chapter 7 Comparative Analysis of Genetic Algorithm, Simulated Annealing and Cutting Angle Method for Artificial Neural Networks
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    Chapter 8 Determining Regularization Parameters for Derivative Free Neural Learning
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    Chapter 9 A Comprehensible SOM-Based Scoring System
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    Chapter 10 The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets
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    Chapter 11 SSC: Statistical Subspace Clustering
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    Chapter 12 Understanding Patterns with Different Subspace Classification
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    Chapter 13 Using Clustering to Learn Distance Functions for Supervised Similarity Assessment
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    Chapter 14 Linear Manifold Clustering
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    Chapter 15 Universal Clustering with Regularization in Probabilistic Space
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    Chapter 16 Acquisition of Concept Descriptions by Conceptual Clustering
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    Chapter 17 Clustering Large Dynamic Datasets Using Exemplar Points
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    Chapter 18 Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files
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    Chapter 19 Alarm Clustering for Intrusion Detection Systems in Computer Networks
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    Chapter 20 Clustering Document Images Using Graph Summaries
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    Chapter 21 Feature Selection Method Using Preferences Aggregation
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    Chapter 22 Ranked Modelling with Feature Selection Based on the CPL Criterion Functions
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    Chapter 23 A Grouping Method for Categorical Attributes Having Very Large Number of Values
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    Chapter 24 Unsupervised Learning of Visual Feature Hierarchies
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    Chapter 25 Multivariate Discretization by Recursive Supervised Bipartition of Graph
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    Chapter 26 CorePhrase: Keyphrase Extraction for Document Clustering
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    Chapter 27 A New Multidimensional Feature Transformation for Linear Classifiers and Its Applications
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    Chapter 28 Comparison of FLDA, MLP and SVM in Diagnosis of Lung Nodule
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    Chapter 29 Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures
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    Chapter 30 Iris Recognition Algorithm Based on Point Covering of High-Dimensional Space and Neural Network
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    Chapter 31 Automatic Clinical Image Segmentation Using Pathological Modelling, PCA and SVM
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    Chapter 32 Improved MRI Mining by Integrating Support Vector Machine Priors in the Bayesian Restoration
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    Chapter 33 Prediction of Secondary Protein Structure Content from Primary Sequence Alone – A Feature Selection Based Approach
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    Chapter 34 Alternative Clustering by Utilizing Multi-objective Genetic Algorithm with Linked-List Based Chromosome Encoding
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    Chapter 35 Embedding Time Series Data for Classification
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    Chapter 36 Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning
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    Chapter 37 Disjunctive Sequential Patterns on Single Data Sequence and Its Anti-monotonicity
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    Chapter 38 Mining Expressive Temporal Associations from Complex Data
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    Chapter 39 Statistical Supports for Frequent Itemsets on Data Streams
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    Chapter 40 Autonomous Vehicle Steering Based on Evaluative Feedback by Reinforcement Learning
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    Chapter 41 Cost Integration in Multi-step Viewpoint Selection for Object Recognition
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    Chapter 42 Support Vector Machine Experiments for Road Recognition in High Resolution Images
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    Chapter 43 An Automatic Face Recognition System in the Near Infrared Spectrum
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    Chapter 44 Hierarchical Partitions for Content Image Retrieval from Large-Scale Database
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    Chapter 45 Optimising the Choice of Colours of an Image Database for Dichromats
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    Chapter 46 An Approach to Mining Picture Objects Based on Textual Cues
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    Chapter 47 Activity and Motion Detection Based on Measuring Texture Change
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    Chapter 48 A New Approach to Human Motion Sequence Recognition with Application to Diving Actions
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    Chapter 49 Dominant Plane Detection Using Optical Flow and Independent Component Analysis
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    Chapter 50 Neural Expert Model Applied to Phonemes Recognition
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    Chapter 51 An Evidential Reasoning Approach to Weighted Combination of Classifiers for Word Sense Disambiguation
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    Chapter 52 Signature-Based Approach for Intrusion Detection
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    Chapter 53 Discovery of Hidden Correlations in a Local Transaction Database Based on Differences of Correlations
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    Chapter 54 An Integrated Approach for Mining Meta-rules
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    Chapter 55 Data Mining on Crash Simulation Data
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    Chapter 56 Pattern Mining Across Domain-Specific Text Collections
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    Chapter 57 Text Classification Using Small Number of Features
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    Chapter 58 Low-Level Cursive Word Representation Based on Geometric Decomposition
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    Chapter 59 Supervised Evaluation of Dataset Partitions: Advantages and Practice
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    Chapter 60 Inference on Distributed Data Clustering
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    Chapter 61 A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases
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    Chapter 62 Mixture Random Effect Model Based Meta-analysis for Medical Data Mining
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    Chapter 63 Semantic Analysis of Association Rules via Item Response Theory
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    Chapter 64 Temporal Approach to Association Rule Mining Using T-Tree and P-Tree
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    Chapter 65 Aquaculture Feature Extraction from Satellite Image Using Independent Component Analysis
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    Chapter 66 Modeling the Organoleptic Properties of Matured Wine Distillates
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    Chapter 67 Bagging Random Trees for Estimation of Tissue Softness
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    Chapter 68 Concept Mining for Indexing Medical Literature
Overall attention for this book and its chapters
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Mentioned by

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78 X users
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1 Google+ user

Citations

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Title
Machine Learning and Data Mining in Pattern Recognition
Published by
Springer Science & Business Media, July 2005
DOI 10.1007/b138149
ISBNs
978-3-54-026923-6, 978-3-54-031891-0
Editors

Perner, Petra, Imiya, Atsushi

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X Demographics

X Demographics

The data shown below were collected from the profiles of 78 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 436 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 436 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 1%
Student > Master 6 1%
Student > Bachelor 3 <1%
Professor 1 <1%
Researcher 1 <1%
Other 0 0%
Unknown 419 96%
Readers by discipline Count As %
Computer Science 9 2%
Engineering 3 <1%
Earth and Planetary Sciences 1 <1%
Business, Management and Accounting 1 <1%
Social Sciences 1 <1%
Other 1 <1%
Unknown 420 96%

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