- Title Pages
- Series Foreword
- Preface
-
1 Introduction to Semi-Supervised Learning -
1 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 Criterion -
12 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 -
25 Analysis of Benchmarks -
22 An Augmented PAC Model for Semi-Supervised Learning -
23 Metric-Based Approaches for Semi-Supervised Regression and Classification -
24 Transductive Inference and Semi-Supervised Learning -
25 A Discussion of Semi-Supervised Learning and Transduction - References
- Notation and Symbols
- Contributors
- Index
Transductive Inference and Semi-Supervised Learning
Transductive Inference and Semi-Supervised Learning
- Chapter:
- (p.452) (p.453) 24 Transductive Inference and Semi-Supervised Learning
- Source:
- Semi-Supervised Learning
- Author(s):
Vapnik Vladimir
- Publisher:
- The MIT Press
This chapter discusses the difference between transductive inference and semi-supervised learning. It argues that transductive inference captures the intrinsic properties of the mechanism for extracting additional information from the unlabeled data. It also shows an important role of transduction for creating noninductive models of inference. In transductive inference the goal is to classify the given u test vectors of interest while in semi-supervised learning the goal is to find the function that minimizes the functional. Semi-supervised learning can be seen as being related to a particular setting of transductive learning. From a conceptual point of view, transductive inference contains important elements of a new philosophy of inference and this is the subject of this discussion.
Keywords: transductive inference, semi-supervised learning, intrinsic properties, unlabeled data, transduction, noninductive models of inference, new philosophy of inference
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- Title Pages
- Series Foreword
- Preface
-
1 Introduction to Semi-Supervised Learning -
1 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 Criterion -
12 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 -
25 Analysis of Benchmarks -
22 An Augmented PAC Model for Semi-Supervised Learning -
23 Metric-Based Approaches for Semi-Supervised Regression and Classification -
24 Transductive Inference and Semi-Supervised Learning -
25 A Discussion of Semi-Supervised Learning and Transduction - References
- Notation and Symbols
- Contributors
- Index
