The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A RKHS Interpolator-Based Graph Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Text classification using string kernels
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A fast technique for comparing graph representations with applications to performance evaluation
International Journal on Document Analysis and Recognition
Graph Database Filtering Using Decision Trees
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A Probabilistic Approach to Learning Costs for Graph Edit Distance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Image recognition for digital libraries
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Graph edit distance with node splitting and merging, and its application to diatom identification
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Fusion of statistical and structural fingerprint classifiers
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Feature selection for graph-based image classifiers
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
An experimental comparison of fingerprint classification methods using graphs
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Decision trees for error-tolerant graph database filtering
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Self-organizing maps for learning the edit costs in graph matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Manifold Learning for Multi-classifier Systems via Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Indexing tree and subtree by using a structure network
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Selecting structural base classifiers for graph-based multiple classifier systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Improving fuzzy multilevel graph embedding through feature selection technique
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Structural pattern representations, especially graphs, have advantages over feature vectors. However, they also suffer from a number of disadvantages, for example, their high computational complexity. Moreover, we observe that in the field of statistical pattern recognition a number of powerful concepts emerged recently that have no equivalent counterpart in the domain of structural pattern recognition yet. Examples include multiple classifier systems and kernel methods. In this paper, we survey a number of recent developments that may be suitable to overcome some of the current limitations of graph based representations in pattern recognition.