Semi-Supervised Learning

Name: Semi-Supervised Learning
Price: 9.82 USD
Availability: InStock
ISBN: 9780262033589

ISBN-10: 0262033585

ISBN-13: 9780262033589

Edition: 2006

Authors: Olivier Chapelle, Alexander Zien, Bernhard Schï¿½lkopf

List price: $55.00

30 day, 100% satisfaction guarantee!

Marketplace

2 new & used from $9.82

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Description:

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learningfirst presents the key assumptions and ideas underlying the field:…

Book details

List price: $55.00
Copyright year: 2006
Publisher: MIT Press
Publication date: 9/22/2006
Binding: Hardcover
Pages: 528
Size: 8.00" wide x 10.00" long x 1.25" tall
Weight: 2.068
Language: English



Series Foreword


Preface



Introduction to Semi-Supervised Learning



Supervised, Unsupervised, and Semi-Supervised Learning



When Can Semi-Supervised Learning Work?



Classes of Algorithms and Organization of This Book



Generative Models



A Taxonomy for Semi-Supervised Learning Methods




The Semi-Supervised Learning Problem



Paradigms for Semi-Supervised Learning



Examples



Conclusions



Semi-Supervised Text Classification Using EM




Introduction



A Generative Model for Text



Experminental Results with Basic EM



Using a More Expressive Generative Model



Overcoming the Challenges of Local Maxima



Conclusions and Summary



Risks of Semi-Supervised Learning




Do Unlabled Data Improve or Degrade Classification Performance?



Understanding Unlabeled Data: Asymptotic Bias



The Asymptotic Analysis of Generative Smei-Supervised Learning



The Value of Labeled and Unlabeled Data



Finite Sample Effects



Model Search and Robustness



Conclusion



Probabilistic Semi-Supervised Cluster with Constraints




Introduction



HMRF Model for Semi-Supervised Clustering



HMRF-KMeans Algorithm



Active Learning for Constraint Acquistion



Experimental Results



Related Work



Conclusions



Low-Density Separation



Transductive Support Vector Machines




Introduction



Transductive Support Vector Machines



Why Use Margin on the Test Set?



Experiments and Applications of the TSVMs



Solving the TSVM Optimization Problem



Connection to Related Approaches



Summary and Conclusions



Semi-Supervised Learning Using Semi-Definite Programming




Relaxing SVM transduction



An Approximation for Speedup



General Semi-Supervised Learning Settings



Empirical Results



Summary and Outlook


Appendix


The Extended Schur Complement Lemma



Gaussian Processes and the Null-Category Noise Model




Introduction



The Noise Model



Process Model and the Effect of the Null-Category



Posterior Inference and Prediction



Results



Discussion



Entropy Regularization




Introduction



Derivation of the Criterion



Optimization Algorithms



Related Methods



Experiments



Conclusion


Appendix


Proof of Theorem 9.1



Data-Dependent Regularization




Introduction



Information Regularization on Metric Spaces



Information Regularization and Relational Data



Discussion



Graph-Based Models



Label Propogation and Quadratic Criterion




Introduction



Label Propogation on a Similarity Graph



Quadratic Cost Criterion



From Transduction to Induction



Incorporating Class Prior Knowledge



Curse of Dimensionality for Semi-Supervised Learning



Discussion



The Geometric Basis of Semi-Supervised Learning




Introduction



Incorporating Geometry in Regularization



Algorithms



Data-Dependent Kernels for Semi-Supervised Learning



Linear Methods for Large-Scale Semi-Supervised Learning



Connections to Other Algorithms and Related Work



Future Directions



Discrete Regularization




Introduction



Discrete Analysis



Discrete Regularization



Conclusion



Semi-Supervised Learning with Conditional Harmonic Mixing




Introduction



Conditional Harmonic Mixing



Learning in CHM Models



Incorporating Prior Knowledge



Learning the Conditionals



Model Averaging



Experiments



Conclusions



Change of Representation



Graph Kernels by Spectral Transforms




The Graph Laplacian



Kernels by Spectral Transforms



Kernel Alignment



Optimizing Alignment Using QCQP for Semi-Supervised Learning



Semi-Supervised Kernels with Order Restraints



Experimental Results



Conclusion



Spectral Methods for Dimensionality Reduction




Introduction



Linear Methods



Graph-Based Methods



Kernel Methods



Discussion



Modifying Distances




Introduction



Estimating DBD Metrics



Computing DBD Metrics



Semi-Supervised Learning Using Density-Based Metrics



Conclusions and Future Work



Semi-Supervised Learning in Practice



Large-Scale Algorithms




Introduction



Cost Approximations



Subset Selection



Discussion



Semi-Supervised Protein Classification Using Cluster Kernels




Introduction



Representation and Kernels for Protein Sequences



Semi-Supervised Kernels for Protein Sequences



Experiments



Discussion



Prediction of Protein Function from Networks




Introduction



Graph-Based Semi-Supervised Learning



Combining Multiple Graphs



Experiments on Function Prediction of Proteins



Conclusion and Outlook



Analysis of Benchmarks



The Benchmark



Application of SSL Methods



Results and Discussion



Perspectives



An Augmented PAC Model for Semi-Supervised Learning




Introduction



A Formal Framework



Sample Complexity Results



Algorithmic Results



Related Models and Discussion



Metric-Based Approaches for Semi-Supervised Regression and Classification




Introduction



Metric Structure of Supervised Learning



Model Selection



Regularization



Classification



Conclusion



Transductive Inference and Semi-Supervised Learning




Problem Settings



Problem of Generalization in Inductive and Transductive Inference



Structure of the VC Bounds and Transductive Inference



The Symmetrization Lemma and Transductive Inference



Bounds for Transductive Inference



The Structural Risk Minimization Principle for Induction and Transduction



Combinatorics in Transductive Inference



Measures of Size of Equivalence Classes



Algorithms for Inductive and Transductive SVMs



Semi-Supervised Learning



Conclusion:


Transductive Inference and the New Problems of Inference



Beyond Transduction: Selective Inference



A Discussion of Semi-Supervised Learning and Transduction


References


Notation and Symbols


Contributors


Index


Online Index