Semi-Supervised Learning
Semi-Supervised Learning
Cite
Abstract
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 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. It first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms which perform two-step learning. It then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of benchmark experiments. Finally, the book looks at interesting directions for SSL research. It closes with a discussion of the relationship between semi-supervised learning and transduction.
-
Front Matter
-
1
Introduction to Semi-Supervised Learning
Chapelle Olivier and others
-
I Generative Models
-
1
A Taxonomy for Semi-Supervised Learning Methods
Seeger Matthias
-
3
Semi-Supervised Text Classification Using EM
Nigam Kamal and others
-
4
Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers
Cozman Fabio andCohen Ira
-
5
Probabilistic Semi-Supervised Clustering with Constraints
Basu Sugato and others
-
1
A Taxonomy for Semi-Supervised Learning Methods
-
II Low-Density Separation
-
6
Transductive Support Vector Machines
Joachims Thorsten
-
7
Semi-Supervised Learning Using Semi-Definite Programming
De Bie Tijl andCristianini Nello
-
8
Gaussian Processes and the Null-Category Noise Model
Lawrence Neil D. andJordan Michael I.
-
9
Entropy Regularization
Grandvalet Yves andBengio Yoshua
-
10
Data-Dependent Regularization
Corduneanu Adrian andJaakkola Tommi
-
6
Transductive Support Vector Machines
-
III Graph-Based Methods
-
11
Label Propagation and Quadratic Criterion
Bengio Yoshua and others
-
12
The Geometric Basis of Semi-Supervised Learning
Sindhwani Vikas and others
-
13
Discrete Regularization
Zhou Dengyong andSchülkopf Bernhard
-
14
Semi-Supervised Learning with Conditional Harmonic Mixing
Burges Christopher J. C. andPlatt John C.
-
11
Label Propagation and Quadratic Criterion
-
IV Change of Representation
-
V Semi-Supervised Learning in Practice
-
VI Perspectives
-
22
An Augmented PAC Model for Semi-Supervised Learning
Balcan Maria-Florina andBlum Avrim
-
23
Metric-Based Approaches for Semi-Supervised Regression and Classification
Schuurmans Dale and others
-
24
Transductive Inference and Semi-Supervised Learning
Vapnik Vladimir
-
25
A Discussion of Semi-Supervised Learning and Transduction
Chapelle Olivier and others
-
22
An Augmented PAC Model for Semi-Supervised Learning
-
End Matter
Sign in
Get help with accessPersonal account
- Sign in with email/username & password
- Get email alerts
- Save searches
- Purchase content
- Activate your purchase/trial code
Institutional access
- Sign in through your institution
- Sign in with a library card Sign in with username/password Recommend to your librarian
Institutional account management
Sign in as administratorPurchase
Our books are available by subscription or purchase to libraries and institutions.
Purchasing informationMonth: | Total Views: |
---|---|
October 2022 | 1 |
October 2022 | 2 |
November 2022 | 2 |
December 2022 | 2 |
January 2023 | 1 |
January 2023 | 3 |
February 2023 | 3 |
March 2023 | 1 |
June 2023 | 2 |
June 2023 | 1 |
June 2023 | 3 |
July 2023 | 1 |
September 2023 | 1 |
September 2023 | 2 |
October 2023 | 2 |
November 2023 | 1 |
November 2023 | 2 |
Get help with access
Institutional access
Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:
IP based access
Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.
Sign in through your institution
Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.
If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.
Sign in with a library card
Enter your library card number to sign in. If you cannot sign in, please contact your librarian.
Society Members
Society member access to a journal is achieved in one of the following ways:
Sign in through society site
Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:
If you do not have a society account or have forgotten your username or password, please contact your society.
Sign in using a personal account
Some societies use Oxford Academic personal accounts to provide access to their members. See below.
Personal account
A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.
Some societies use Oxford Academic personal accounts to provide access to their members.
Viewing your signed in accounts
Click the account icon in the top right to:
Signed in but can't access content
Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.
Institutional account management
For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.