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Machine Learning and Data Mining in Pattern Recognition

Overview of attention for book
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 Efficient Error Setting for Subspace Miners
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    Chapter 2 Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier
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    Chapter 3 A Cost-Sensitive Based Approach for Improving Associative Classification on Imbalanced Datasets
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    Chapter 4 Multiple Regression Method Based on Unexpandable and Irreducible Convex Combinations
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    Chapter 5 A Novel Approach for Identifying Banded Patterns in Zero-One Data Using Column and Row Banding Scores
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    Chapter 6 Fast Pattern Recognition and Deep Learning Using Multi-Rooted Binary Decision Diagrams
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    Chapter 7 ACCD: Associative Classification over Concept-Drifting Data Streams
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    Chapter 8 Integrating Weight with Ensemble to Handle Changes in Class Distribution
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    Chapter 9 A Robust One-Class Classification Model with Interval-Valued Data Based on Belief Functions and Minimax Strategy
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    Chapter 10 Manifold Learning in Data Mining Tasks
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    Chapter 11 Adaptive Multiple-Resolution Stream Clustering
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    Chapter 12 Monitoring Distributed Data Streams through Node Clustering
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    Chapter 13 Minimizing Cluster Errors in LP-Based Nonlinear Classification
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    Chapter 14 CBOF: Cohesiveness-Based Outlier Factor A Novel Definition of Outlier-ness
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    Chapter 15 A New Measure of Outlier Detection Performance
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    Chapter 16 A Multi-path Strategy for Hierarchical Ensemble Classification
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    Chapter 17 Label Correction Strategy on Hierarchical Multi-Label Classification
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    Chapter 18 Towards Time Series Classification without Human Preprocessing
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    Chapter 19 Applications of Concurrent Sequential Patterns in Protein Data Mining
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    Chapter 20 Semi-supervised Time Series Modeling for Real-Time Flux Domain Detection on Passive DNS Traffic
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    Chapter 21 Dictionary Learning-Based Volumetric Image Classification for the Diagnosis of Age-Related Macular Degeneration
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    Chapter 22 A Robot Waiter Learning from Experiences
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    Chapter 23 Parametric Representation of Objects in Color Space Using One-Class Classifiers
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    Chapter 24 An Automatic Matching Procedure of Ultrasonic Railway Defectograms
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    Chapter 25 Open Issues on Codebook Generation in Image Classification Tasks
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    Chapter 26 A Neuro-Genetic System for Cardiac Arrhythmia Classification
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    Chapter 27 Finding Multi-dimensional Patterns in Healthcare
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    Chapter 28 A Generalized Relationship Mining Method for Social Media Text Data
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    Chapter 29 Clustering Students Based on Student’s Performance - A Partial Least Squares Path Modeling (PLS-PM) Study
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    Chapter 30 Learning of Natural Trading Strategies on Foreign Exchange High-Frequency Market Data Using Dynamic Bayesian Networks
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    Chapter 31 Mining Users Playbacks History for Music Recommendations
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    Chapter 32 A Method of Crowd-Sourced Information Extraction From Large Data Files
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    Chapter 33 Modified Bootstrap Approach with State Number Optimization for Hidden Markov Model Estimation in Small-Size Printed Arabic Text Line Recognition
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    Chapter 34 The Nearest Centroid Based on Vector Norms: A New Classification Algorithm for a New Document Representation Model
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    Chapter 35 SimiLay: A Developing Web Page Layout Based Visual Similarity Search Engine
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    Chapter 36 Record Linkage Using Graph Consistency
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    Chapter 37 Investigating Long Short-Term Memory Networks for Various Pattern Recognition Problems
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    Chapter 38 Resolution of Geographical String Name through Spatio-Temporal Information
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    Chapter 39 Detecting Programming Language from Source Code Using Bayesian Learning Techniques
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    Chapter 40 Creation of Bi-lingual Social Network Dataset Using Classifiers
Overall attention for this book and its chapters
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Citations

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Readers on

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Title
Machine Learning and Data Mining in Pattern Recognition
Published by
Lecture notes in computer science, January 2014
DOI 10.1007/978-3-319-08979-9
ISBNs
978-3-31-908978-2, 978-3-31-908979-9
Authors

Petra Perner

Editors

Petra Perner

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 4%
Student > Bachelor 1 4%
Student > Doctoral Student 1 4%
Unknown 20 87%
Readers by discipline Count As %
Computer Science 2 9%
Engineering 1 4%
Unknown 20 87%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 June 2015.
All research outputs
#20,278,422
of 22,811,321 outputs
Outputs from Lecture notes in computer science
#6,985
of 8,124 outputs
Outputs of similar age
#265,000
of 305,455 outputs
Outputs of similar age from Lecture notes in computer science
#236
of 280 outputs
Altmetric has tracked 22,811,321 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 305,455 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 280 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.