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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

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Greedy Graph Edit Distance
  3. Altmetric Badge
    Chapter 2 Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation
  4. Altmetric Badge
    Chapter 3 Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning
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    Chapter 4 Local and Global Genetic Fuzzy Pattern Classifiers
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    Chapter 5 IKLTSA: An Incremental Kernel LTSA Method
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    Chapter 6 SentiSAIL: Sentiment Analysis in English, German and Russian
  8. Altmetric Badge
    Chapter 7 Sentiment Analysis for Government: An Optimized Approach
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    Chapter 8 A Novel Algorithm for the Integration of the Imputation of Missing Values and Clustering
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    Chapter 9 Improving the Algorithm for Mapping of OWL to Relational Database Schema
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    Chapter 10 Robust Principal Component Analysis of Data with Missing Values
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    Chapter 11 Efficient Mining of High-Utility Sequential Rules
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    Chapter 12 MOGACAR: A Method for Filtering Interesting Classification Association Rules
  14. Altmetric Badge
    Chapter 13 Classifying Grasslands and Cultivated Pastures in the Brazilian Cerrado Using Support Vector Machines, Multilayer Perceptrons and Autoencoders
  15. Altmetric Badge
    Chapter 14 Hybrid Approach for Inductive Semi Supervised Learning using Label Propagation and Support Vector Machine
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    Chapter 15 Optimizing the Data-Process Relationship for Fast Mining of Frequent Itemsets in MapReduce
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    Chapter 16 Aggregation-Aware Compression of Probabilistic Streaming Time Series
  18. Altmetric Badge
    Chapter 17 Applying Clustering Analysis to Heterogeneous Data Using Similarity Matrix Fusion (SMF)
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    Chapter 18 On Bicluster Aggregation and its Benefits for Enumerative Solutions
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    Chapter 19 Semi-Supervised Stream Clustering Using Labeled Data Points
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    Chapter 20 Avalanche: A Hierarchical, Divisive Clustering Algorithm
  22. Altmetric Badge
    Chapter 21 Author Attribution of Email Messages Using Parse-Tree Features
  23. Altmetric Badge
    Chapter 22 Robust Principal Component Analysis of Data with Missing Values
  24. Altmetric Badge
    Chapter 23 Offline Writer Identification in Tamil Using Bagged Classification Trees
  25. Altmetric Badge
    Chapter 24 Data Analysis for Courses Registration
  26. Altmetric Badge
    Chapter 25 Learning the Relationship Between Corporate Governance and Company Performance Using Data Mining
  27. Altmetric Badge
    Chapter 26 A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization
  28. Altmetric Badge
    Chapter 27 Data Driven Geometry for Learning
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    Chapter 28 Mining Educational Data to Predict Students’ Academic Performance
  30. Altmetric Badge
    Chapter 29 Patient-Specific Modeling of Medical Data
  31. Altmetric Badge
    Chapter 30 A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis
  32. Altmetric Badge
    Chapter 31 Automatic Cell Tracking and Kinetic Feature Description of Cell Paths for Image Mining
Attention for Chapter 14: Hybrid Approach for Inductive Semi Supervised Learning using Label Propagation and Support Vector Machine
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Mentioned by

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1 X user

Citations

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Summary X Dimensions citations
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Chapter title
Hybrid Approach for Inductive Semi Supervised Learning using Label Propagation and Support Vector Machine
Chapter number 14
Book title
Machine Learning and Data Mining in Pattern Recognition
Published in
arXiv, December 2015
DOI 10.1007/978-3-319-21024-7_14
Book ISBNs
978-3-31-921023-0, 978-3-31-921024-7
Authors

Aruna Govada, Pravin Joshi, Sahil Mittal, Sanjay K Sahay, Sanjay K. Sahay

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 13%
Unknown 7 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Student > Master 2 25%
Lecturer 1 13%
Researcher 1 13%
Unknown 2 25%
Readers by discipline Count As %
Computer Science 5 63%
Engineering 1 13%
Unknown 2 25%
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 08 December 2015.
All research outputs
#20,297,343
of 22,834,308 outputs
Outputs from arXiv
#670,821
of 937,460 outputs
Outputs of similar age
#324,913
of 387,655 outputs
Outputs of similar age from arXiv
#8,306
of 13,134 outputs
Altmetric has tracked 22,834,308 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 937,460 research outputs from this source. They receive a mean Attention Score of 3.9. 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 387,655 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 13,134 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.

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