A Step Towards Unification of Syntactic and Statistical Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special memorial issue for Professor King-Sun Fu
Pattern Recognition
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Shape recognition from large image libraries by inexact graph matching
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Matching and Indexing of Graph Models in Content-Based Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Weighted mean of a pair of graphs
Computing
Comparing Structures Using a Hopfield-Style Neural Network
Applied Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A RKHS Interpolator-Based Graph Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Using Graph Search Techniques for Contextual Colour Retrieval
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Improved Simulated Annealing, Boltzmann Machine, and Attributed Graph Matching
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Computing approximate tree edit distance using relaxation labeling
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Automorphism Partitioning with Neural Networks
Neural Processing Letters
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact and Approximate Graph Matching Using Random Walks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Protein function prediction via graph kernels
Bioinformatics
Median graph computation for graph clustering
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Graph-Theoretic Techniques for Web Content Mining
Graph-Theoretic Techniques for Web Content Mining
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Replicator Equations, Maximal Cliques, and Graph Isomorphism
Neural Computation
A Binary Linear Programming Formulation of the Graph Edit Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Riemannian approach to graph embedding
Pattern Recognition
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
HMM-based graph edit distance for image indexing
International Journal of Imaging Systems and Technology - Multimedia Information Retrieval
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Reducing the dimensionality of dissimilarity space embedding graph kernels
Engineering Applications of Artificial Intelligence
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bridging the Gap Between Graph Edit Distance and Kernel Machines
A Labelled Graph Based Multiple Classifier System
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
The graph neural network model
IEEE Transactions on Neural Networks
Neural network for graphs: a contextual constructive approach
IEEE Transactions on Neural Networks
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Kernels For Structured Data
Entropy and Distance of Random Graphs with Application to Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Comparing Relational Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast suboptimal algorithms for the computation of graph edit distance
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Transforming strings to vector spaces using prototype selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Reactive tabu search for measuring graph similarity
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Genetic-based search for error-correcting graph isomorphism
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organizing maps for learning the edit costs in graph matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Graph embedding in vector spaces by node attribute statistics
Pattern Recognition
A comparison between structural and embedding methods for graph classification
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Discriminative prototype selection methods for graph embedding
Pattern Recognition
Efficient geometric graph matching using vertex embedding
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A new proposal for graph classification using frequent geometric subgraphs
Data & Knowledge Engineering
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The field of pattern recognition is usually subdivided into the statistical and the structural approach. Structural pattern recognition allows one to use powerful and flexible representation formalisms but offers only a limited repertoire of algorithmic tools needed to solve classification and clustering problems. By contrast, the statistical approach is mathematically well founded and offers many tools, but provides a representation formalism that is limited in its power and flexibility. Hence, both subfields are complementary to each other. During the last three decades several efforts have been made towards bridging the gap between structural and statistical pattern recognition in order to profit from the benefits of each approach and eliminate the drawbacks. The present paper reviews some of these attempts made towards the unification of structural and statistical pattern recognition and analyzes the progress that has been achieved.