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The Dissimilarity Representation For Pattern Recognition : Foundations And Applications.
This book provides a fundamentally new approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This 'dissimilarity representation' bridges the gap between the traditionally opposing approaches of statistical a...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore :
World Scientific,
2005.
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| Colección: | Series in machine perception and artificial intelligence ;
v. 64. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 1. Introduction. 1.1. Recognizing the pattern. 1.2. Dissimilarities for representation. 1.3. Learning from examples. 1.4. Motivation of the use of dissimilarity representations. 1.5. Relation to kernels. 1.6. Outline of the book. 1.7. In summary
- 2. Spaces. 2.1. Preliminaries. 2.2. A brief look at spaces. 2.3. Generalized topological spaces. 2.4. Generalized metric spaces. 2.5. Vector spaces. 2.6. Normed and inner product spaces. 2.7. Indefinite inner product spaces. 2.8. Discussion
- 3. Characterization of dissimilarities. 3.1. Embeddings, tree models and transformations. 3.2. Tree models for dissimilarities. 3.3. Useful transformations. 3.4. Properties of dissimilarity matrices. 3.5. Linear embeddings of dissimilarities. 3.6. Spatial representation of dissimilarities. 3.7. Summary
- 4. Learning approaches. 4.1. Traditional learning. 4.2. The role of dissimilarity representations. 4.3. Classification in generalized topological spaces. 4.4. Classification in dissimilarity spaces. 4.5. Classification in pseudo-Euclidean spaces. 4.6. On generalized kernels and dissimilarity spaces. 4.7. Discussion
- 5. Dissimilarity measures. 5.1. Measures depending on feature types. 5.2. Measures between populations. 5.3. Dissimilarity measures between sequences. 5.4. Information-theoretic measures. 5.5. Dissimilarity measures between sets. 5.6. Dissimilarity measures in applications. 5.7. Discussion and conclusions
- 6. Visualization. 6.1. Multidimensional scaling. 6.2. Other mappings. 6.3. Examples : getting insight into the data. 6.4. Tree models. 6.5. Summary
- 7. Flirther data exploration. 7.1. Clustering. 7.2. Intrinsic dimension. 7.3. Sampling density. 7.4. Summary
- 8. One-class classifiers. 8.1. General issues. 8.2. Domain descriptors for dissimilarity representations. 8.3. Experiments. 8.4. Conclusions
- 9. Classification. 9.1. Proof of principle. 9.2. Selection of the representation set : the dissimilarity space approach. 9.3. Selection of the representation set : the embedding approach. 9.4. On corrections of dissimilarity measures. 9.5. A few remarks on a simulated missing value problem. 9.6. Existence of zero-error dissimilarity-based classifiers. 9.7. Final discussion
- 10. Combining. 10.1. Combining for one-class classification. 10.2. Combining for standard two-class classification. 10.3. Classifier projection space. 10.4. Summary
- 11. Representation review and recommendations. 11.1. Representation review. 11.2. Practical considerations
- 12. Conclusions and open problems. 12.1. Summary and contributions. 12.2. Extensions of dissimilarity representations. 12.3. Open questions.