When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Improving Graph Classification by Isomap
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
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
Using kernels on hierarchical graphs in automatic classification of designs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Superposition and Alignment of Labeled Point Clouds
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
Graph embedding in vector spaces by node attribute statistics
Pattern Recognition
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
Annals of Mathematics and Artificial Intelligence
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This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers. Contents: Why Kernels for Structured Data?; Kernel Methods in a Nutshell; Kernell Design; Basic Term Kernels; Graph Kernels.