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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 Quadratically Constrained Maximum a Posteriori Estimation for Binary Classifier
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    Chapter 2 Hubness-Based Fuzzy Measures for High-Dimensional k -Nearest Neighbor Classification
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    Chapter 3 Decisions: Algebra and Implementation
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    Chapter 4 Smoothing Multinomial Naïve Bayes in the Presence of Imbalance
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    Chapter 5 ACE-Cost: Acquisition Cost Efficient Classifier by Hybrid Decision Tree with Local SVM Leaves
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    Chapter 6 Informative Variables Selection for Multi-relational Supervised Learning
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    Chapter 7 Separability of Split Value Criterion with Weighted Separation Gains
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    Chapter 8 Granular Instances Selection for Fuzzy Modeling
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    Chapter 9 Parameter-Free Anomaly Detection for Categorical Data
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    Chapter 10 Fuzzy Semi-supervised Support Vector Machines
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    Chapter 11 GENCCS: A Correlated Group Difference Approach to Contrast Set Mining
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    Chapter 12 Collective Classification Using Heterogeneous Classifiers
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    Chapter 13 Spherical Nearest Neighbor Classification: Application to Hyperspectral Data
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    Chapter 14 Adaptive Kernel Diverse Density Estimate for Multiple Instance Learning
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    Chapter 15 Boosting Inspired Process for Improving AUC
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    Chapter 16 Investigation in Transfer Learning: Better Way to Apply Transfer Learning between Agents
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    Chapter 17 Exploration Strategies for Learned Probabilities in Smart Terrain
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    Chapter 18 Sensitivity Analysis for Weak Constraint Generation
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    Chapter 19 Dictionary Learning Based on Laplacian Score in Sparse Coding
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    Chapter 20 A Practical Approach for Clustering Transaction Data
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    Chapter 21 Hierarchical Clustering with High Order Dissimilarities
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    Chapter 22 Clust-XPaths: Clustering of XML Paths
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    Chapter 23 Comparing Clustering and Metaclustering Algorithms
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    Chapter 24 Detection of Phenotypes in Microarray Data Using Force- Directed Placement Transformss
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    Chapter 25 On the Temporal Behavior of EEG Recorded during Real Finger Movement
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    Chapter 26 A Machine Learning and Data Mining Framework to Enable Evolutionary Improvement in Trauma Triage
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    Chapter 27 A Decision Support System Based on the Semantic Analysis of Melanoma Images Using Multi-elitist PSO and SVM
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    Chapter 28 Authorship Similarity Detection from Email Messages
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    Chapter 29 An Investigation Concerning the Generation of Text Summarisation Classifiers Using Secondary Data
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    Chapter 30 Comparing the One-vs-One and One-vs-All Methods in Benthic Macroinvertebrate Image Classification
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    Chapter 31 Incremental Web-Site Boundary Detection Using Random Walks
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    Chapter 32 Discovering Text Patterns by a New Graphic Model
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    Chapter 33 Topic Sentiment Change Analysis
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    Chapter 34 Adaptive Context Modeling for Deception Detection in Emails
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    Chapter 35 Contrasting Correlations by an Efficient Double-Clique Condition
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    Chapter 36 Estimating Image Segmentation Difficulty
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    Chapter 37 Mining Spatial Trajectories Using Non-parametric Density Functions
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    Chapter 38 Exploring Synergetic Effects of Dimensionality Reduction and Resampling Tools on Hyperspectral Imagery Data Classification
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    Chapter 39 A Comparison between Haralick´s Texture Descriptor and the Texture Descriptor Based on Random Sets for Biological Images
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    Chapter 40 Unsupervised Discovery of Motifs under Amplitude Scaling and Shifting in Time Series Databases
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    Chapter 41 Static Load Balancing of Parallel Mining of Frequent Itemsets Using Reservoir Sampling
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    Chapter 42 GA-TVRC: A Novel Relational Time Varying Classifier to Extract Temporal Information Using Genetic Algorithms
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    Chapter 43 Detection of Communities and Bridges in Weighted Networks
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    Chapter 44 Techniques for Improving Filters in Power Grid Contingency Analysis
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Title
Machine Learning and Data Mining in Pattern Recognition
Published by
Springer Berlin Heidelberg, August 2011
DOI 10.1007/978-3-642-23199-5
ISBNs
978-3-64-223198-8, 978-3-64-223199-5
Editors

Perner, Petra

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 428 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 428 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 <1%
Student > Doctoral Student 1 <1%
Unknown 426 100%
Readers by discipline Count As %
Computer Science 2 <1%
Unknown 426 100%