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Structural, Syntactic, and Statistical Pattern Recognition

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Cover of 'Structural, Syntactic, and Statistical Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 A Graph Kernel from the Depth-Based Representation
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    Chapter 2 Incorporating Molecule’s Stereisomerism within the Machine Learning Framework
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    Chapter 3 Transitive State Alignment for the Quantum Jensen-Shannon Kernel
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    Chapter 4 Balanced K-Means for Clustering
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    Chapter 5 Poisoning Complete-Linkage Hierarchical Clustering
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    Chapter 6 A Comparison of Categorical Attribute Data Clustering Methods
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    Chapter 7 Improving Approximate Graph Edit Distance Using Genetic Algorithms
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    Chapter 8 Approximate Graph Edit Distance Guided by Bipartite Matching of Bags of Walks
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    Chapter 9 A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance
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    Chapter 10 Flip-Flop Sublinear Models for Graphs
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    Chapter 11 Node Centrality for Continuous-Time Quantum Walks
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    Chapter 12 Max-Correlation Embedding Computation
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    Chapter 13 Fast Gradient Computation for Learning with Tensor Product Kernels and Sparse Training Labels
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    Chapter 14 Nonlinear Discriminant Analysis Based on Probability Estimation by Gaussian Mixture Model
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    Chapter 15 Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood
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    Chapter 16 Majority Vote of Diverse Classifiers for Late Fusion
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    Chapter 17 Entropic Graph Embedding via Multivariate Degree Distributions
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    Chapter 18 On Parallel Lines in Noisy Forms
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    Chapter 19 Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance
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    Chapter 20 Matching Similarity for Keyword-Based Clustering
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    Chapter 21 Quantum vs Classical Ranking in Segment Grouping
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    Chapter 22 Remove Noise in Video with 3D Topological Maps
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    Chapter 23 Video Analysis of a Snooker Footage Based on a Kinematic Model
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    Chapter 24 Evaluating Classification Performance with only Positive and Unlabeled Samples
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    Chapter 25 Who Is Missing? A New Pattern Recognition Puzzle
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    Chapter 26 Edit Distance Computed by Fast Bipartite Graph Matching
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    Chapter 27 Statistical Method for Semantic Segmentation of Dominant Plane from Remote Exploration Image Sequence
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    Chapter 28 Analyses on Generalization Error of Ensemble Kernel Regressors
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    Chapter 29 Structural Human Shape Analysis for Modeling and Recognition
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    Chapter 30 On Cross-Validation for MLP Model Evaluation
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    Chapter 31 Weighted Mean Assignment of a Pair of Correspondences Using Optimisation Functions
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    Chapter 32 Chemical Symbol Feature Set for Handwritten Chemical Symbol Recognition
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    Chapter 33 About Combining Metric Learning and Prototype Generation
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    Chapter 34 Tracking System with Re-identification Using a RGB String Kernel
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    Chapter 35 Towards Scalable Prototype Selection by Genetic Algorithms with Fast Criteria
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    Chapter 36 IOWA Operators and Its Application to Image Retrieval
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    Chapter 37 On Optimum Thresholding of Multivariate Change Detectors
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    Chapter 38 Commute Time for a Gaussian Wave Packet on a Graph
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    Chapter 39 Properties of Object-Level Cross-Validation Schemes for Symmetric Pair-Input Data
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    Chapter 40 A Binary Factor Graph Model for Biclustering
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    Chapter 41 Improved BLSTM Neural Networks for Recognition of On-Line Bangla Complex Words
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    Chapter 42 A Ranking Part Model for Object Detection
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    Chapter 43 Regular Decomposition of Multivariate Time Series and Other Matrices
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    Chapter 44 Texture Synthesis: From Convolutional RBMs to Efficient Deterministic Algorithms
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    Chapter 45 Improved Object Matching Using Structural Relations
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    Chapter 46 Designing LDPC Codes for ECOC Classification Systems
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    Chapter 47 Unifying Probabilistic Linear Discriminant Analysis Variants in Biometric Authentication
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Title
Structural, Syntactic, and Statistical Pattern Recognition
Published by
Springer, Berlin, Heidelberg, January 2014
DOI 10.1007/978-3-662-44415-3
ISBNs
978-3-66-244414-6, 978-3-66-244415-3
Editors

Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo

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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 30%
Professor > Associate Professor 2 20%
Researcher 2 20%
Student > Postgraduate 1 10%
Student > Master 1 10%
Other 1 10%
Readers by discipline Count As %
Computer Science 4 40%
Mathematics 2 20%
Environmental Science 1 10%
Agricultural and Biological Sciences 1 10%
Physics and Astronomy 1 10%
Other 1 10%