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Semi-supervised learning /
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems, this text looks at state-of-the-art algorithms, applications benchmark experiments, and directions for future research.
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Mass. :
MIT Press,
©2006.
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| Colección: | Adaptive computation and machine learning.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Series Foreword; Preface; 1
- Introduction to Semi-Supervised Learning; 2
- A Taxonomy for Semi-Supervised Learning Methods; 3
- Semi-Supervised Text Classification Using EM; 4
- Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5
- Probabilistic Semi-Supervised Clustering with Constraints; 6
- Transductive Support Vector Machines; 7
- Semi-Supervised Learning Using Semi- Definite Programming; 8
- Gaussian Processes and the Null-Category Noise Model; 9
- Entropy Regularization; 10
- Data-Dependent Regularization.
- 11
- Label Propagation and Quadratic Criterion12
- The Geometric Basis of Semi-Supervised Learning; 13
- Discrete Regularization; 14
- Semi-Supervised Learning with Conditional Harmonic Mixing; 15
- Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17
- Modifying Distances; 18
- Large-Scale Algorithms; 19
- Semi-Supervised Protein Classification Using Cluster Kernels; 20
- Prediction of Protein Function from Networks; 21
- Analysis of Benchmarks; 22
- An Augmented PAC Model for Semi- Supervised Learning.
- 23
- Metric-Based Approaches for Semi- Supervised Regression and Classification24
- Transductive Inference and Semi-Supervised Learning; 25
- A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index.