| Description |
1 online resource. |
|
text txt rdacontent |
|
computer c rdamedia |
|
online resource cr rdacarrier |
| Series |
Synthesis lectures on human language technologies ; #21. 1947-4040
|
| Reproduction |
Electronic reproduction. Perth, W.A. Available via World Wide Web. |
| Note |
Online resource; title from PDF title page (Morgan & Claypool, viewed on June 15, 2013). |
| Bibliography |
Includes bibliographical references (pages 81-92). |
| Contents |
1. Introduction -- 1.1 Introduction -- 1.2 Learning under bias -- 1.3 Empirical evaluations. |
|
2. Supervised and unsupervised prediction -- 2.1 Standard assumptions in supervised learning -- 2.1.1 How to check whether the assumptions hold -- 2.2 Nearest neighbor -- 2.3 Naive Bayes -- 2.4 Perceptron -- 2.4.1 Large-margin methods -- 2.5 Comparisons of classification algorithms -- 2.6 Learning from weighted data -- 2.6.1 Weighted k-nearest neighbor -- 2.6.2 Weighted naive Bayes -- 2.6.3 Weighted perceptron -- 2.6.4 Weighted large-margin learning -- 2.7 Clustering algorithms -- 2.7.1 Hierarchical clustering -- 2.7.2 k-means -- 2.7.3 Expectation maximization -- 2.7.4 Evaluating clustering algorithms -- 2.8 Part-of-speech tagging -- 2.9 Dependency parsing -- 2.9.1 Transition-based dependency parsing -- 2.9.2 Graph-based dependency parsing. |
|
3. Semi-supervised learning -- 3.1 Wrapper methods -- 3.1.1 Self-training -- 3.1.2 Co-training -- 3.1.3 Tri-training -- 3.1.4 Soft self-training, EM and co-EM -- 3.2 Clusters-as-features -- 3.3 Semi-supervised nearest neighbor -- 3.3.1 Label propagation -- 3.3.2 Semi-supervised nearest neighbor editing -- 3.3.3 Semi-supervised condensed nearest neighbor. |
|
4. Learning under bias -- 4.1 Semi-supervised learning as transfer learning -- 4.2 Transferring data -- 4.2.1 Outlier detection -- 4.2.2 Importance weighting -- 4.3 Transferring features -- 4.3.1 Changing feature representation to minimize divergence -- 4.3.2 Structural correspondence learning -- 4.4 Transferring parameters. |
|
5. Learning under unknown bias -- 5.1 Adversarial learning -- 5.2 Ensemble-based methods and meta-learning. |
|
6. Evaluating under bias -- 6.1 What is language? -- 6.2 Significance across corpora -- 6.3 Meta-analysis -- 6.4 Performance and data characteristics -- 6.5 Down-stream evaluation. |
|
Bibliography -- Author's biography. |
| Indexed In: |
Compendex |
|
INSPEC |
|
Google scholar |
|
Google book search |
| Subject |
Natural language processing (Computer science)
|
|
Supervised learning (Machine learning)
|
|
natural language processing |
|
machine learning |
|
learning under bias |
|
semi-supervised learning |
| Added Author |
Ebooks Corporation
|
| Related To |
Print version: Søgaard, Anders, 1981- Semi-supervised learning and domain adaptation in natural language processing. [San Rafael, Calif.] : Morgan & Claypool Publishers, ©2013 1608459853 |
| ISBN |
9781608459865 (electronic bk.) |
|
1608459861 (electronic bk.) |
|
9781608459858 (pbk.) |
| UPC # |
10.2200/S00497ED1V01Y201304HLT021 doi |
| OCLC # |
EBC1207304 |
|
