Grammatical inference: learning automata and grammars
Gespeichert in:
1. Verfasser: | |
---|---|
Format: | Buch |
Sprache: | English |
Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Pr.
2010
|
Ausgabe: | 1. publ. |
Schlagworte: | |
Online-Zugang: | Inhaltsverzeichnis |
Beschreibung: | XIII, 416 S. graph. Darst. |
ISBN: | 9780521763165 |
Internformat
MARC
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245 | 1 | 0 | |a Grammatical inference |b learning automata and grammars |c Colin de la Higuera |
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264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Pr. |c 2010 | |
300 | |a XIII, 416 S. |b graph. Darst. | ||
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650 | 4 | |a Formal languages | |
650 | 4 | |a Logic, Symbolic and mathematical | |
650 | 4 | |a Formal languages | |
650 | 4 | |a Logic, Symbolic and mathematical | |
650 | 0 | 7 | |a Inferenz |g Künstliche Intelligenz |0 (DE-588)4333533-0 |2 gnd |9 rswk-swf |
650 | 0 | 7 | |a Formale Grammatik |0 (DE-588)4154991-0 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
_version_ | 1804141162341072896 |
---|---|
adam_text | Contents
Preface
page
ix
Acknowledgements
xiv
Introduction
1
1.1
The field
1
1.2
An introductory example
6
1.3
Why is learning grammars hard?
17
1.4
About identification, approximation and in a general sense
convergence
18
1.5
Organisation of the manuscript
21
1.6
Conclusions of the chapter and further reading
23
The data and some applications
27
2.1
Linguistic data and applications
27
2.2
Biological data and applications
30
2.3
Data and applications in pattern recognition
32
2.4
Data in computer science applications
34
2.5
Other situations
37
2.6
Conclusions of the chapter and further reading
40
Part I The Tools
43
3
Basic stringology
45
3.1
Notations
45
3.2
Alphabets, strings and languages
48
3.3
Trees and terms
51
3.4
Distances between strings
54
3.5
String kernels
60
3.6
Some simple classes of languages
64
3.7
Exercises
65
3.8
Conclusions of the chapter and further reading
67
4
Representing languages
70
4.1
Automata and finite state machines
70
vj
Contents
4.2
Grammars
4.3
Exercises
82
4.4
Conclusions of the chapter and further reading
82
5
Representing distributions over strings with automata and grammars
86
5.1
Distributions over strings
86
5.2
Probabilistic automata
87
5.3
Probabilistic context-free grammars
100
5.4
Distances between two distributions
102
5.5
Computing distances
107
5.6
Exercises HI
5.7
Conclusions of the chapter and further reading
112
6
About combinatorics
116
6.1
About VC-dimensions
116
6.2
About consistency
119
6.3
The search space for the DFA learning problem
123
6.4
About the equivalence problem and its relation to characteristic sets
131
6.5
Some remarkable automata
132
6.6
Exercises
136
6.7
Conclusions of the chapter and further reading
137
Partii
What Does Learning a Language Mean?
141
7
Identifying languages
143
7.1
Introductory discussion
143
7.2
Identification in the limit and variants
146
7.3
Complexity aspects of identification in the limit
152
7.4
Commuting diagrams
156
7.5
Active learning
162
7.6
Learning with noise
166
7.7
Exercises
169
7.8
Conclusions of the chapter and further reading
170
8
Learning from text
173
8.1
Identification in the limit from text
173
8.2
Exercises
181
8.3
Conclusions of the chapter and further reading
181
9
Active learning
184
9.1
About learning with queries
184
9.2
Learning from membership queries alone
188
9.3
Learning from equivalence queries alone
188
9.4
PAC
active learning results
191
9.5
Exercises
192
9.6
Conclusions of the chapter and further reading
193
Contents
vii
10
Learning distributions over strings
196
10.1
About sampling
197
10.2
Some bounds
198
10.3
PAC-learning languages
200
10.4
PAC-learning from text
200
10.5
Identification in the limit with probability one
201
10.6
PAC-learning distributions
207
10.7
Learning distributions with queries
208
10.8
Exercises
210
10.9
Conclusions of the chapter and further reading
211
Partili
Learning Algorithms and Techniques
215
11
Text learners
217
11.1
Window languages
217
11.2
Look-ahead languages
223
11.3
Pattern languages
230
11.4
Planar languages
232
11.5
Exercises
234
11.6
Conclusions of the chapter and further reading
235
12
Informed learners
237
12.1
The prefix tree acceptor
(РТА)
23 8
12.2
The basic operations
240
12.3
Gold s algorithm
243
12.4
RPNI
255
12.5
Exercises
265
12.6
Conclusions of the chapter and further reading
266
13
Learning with queries
269
13.1
The minimally adequate teacher
269
13.2
The algorithm
274
13.3
Exercises
278
13.4
Conclusions of the chapter and further reading
279
14
Artificial intelligence techniques
281
14.1
A survey of some artificial intelligence ideas
282
14.2
Genetic algorithms
283
14.3
Tabu search
286
14.4 MDL
principle in grammatical inference
289
14.5
Heuristic greedy state merging
292
14.6
Graph colouring and constraint satisfaction
295
14.7
Exercises
297
14.8
Conclusions of the chapter and further reading
298
vji¡
Contents
15
Learning context-free grammars
300
15.1
The difficulties 301
15.2
Learning reversible context-free grammars
307
15.3
Constructive rewriting systems
313
15.4
Reducing rewriting systems
315
15.5
Some heuristics
320
15.6
Exercises
323
15.7
Conclusions of the chapter and further reading
323
16
Learning probabilistic finite automata
329
16.1
Issues
329
16.2
Probabilities and frequencies
330
16.3
State merging algorithms
333
16.4
ALERGIA
339
16.5
Using distinguishing strings
345
16.6
Hardness results regarding
ALERGIA
and DSAI
349
16.7
MDI
and other heuristics
351
16.8
Exercises
353
16.9
Conclusions of the chapter and further reading
353
17
Estimating the probabilities
357
17.1
The deterministic case
358
17.2
Towards non-determinism
360
17.3
The EM algorithm
361
17.4
The
Baum-
Welch algorithm
362
17.5
The INSIDE-OUTSIDE algorithm
367
17.6
Exercises
368
17.7
Conclusions of the chapter and further reading
368
18
Learning transducers
372
18.1
Bilanguages
372
18.2
OSTIA, a
first algorithm that learns transducers
376
18.3 OSTIA 379
18.4
Identifying partial functions
387
18.5
Exercises
388
18.6
Conclusions of the chapter and further reading
388
19
A very small conclusion
391
19.1
About convergence
391
19.2
About complexity
392
19.3
About trees and graphs and more structure
392
19.4
About applications
393
19.5
About learning itself
393
References
394
Index 4J4
|
any_adam_object | 1 |
author | De la Higuera, Colin 1962- |
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dewey-raw | 005.131 |
dewey-search | 005.131 |
dewey-sort | 15.131 |
dewey-tens | 000 - Computer science, information, general works |
discipline | Informatik Sprachwissenschaft Literaturwissenschaft |
edition | 1. publ. |
format | Book |
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spelling | De la Higuera, Colin 1962- Verfasser (DE-588)141231386 aut Grammatical inference learning automata and grammars Colin de la Higuera 1. publ. Cambridge [u.a.] Cambridge Univ. Pr. 2010 XIII, 416 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Formal languages Logic, Symbolic and mathematical Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd rswk-swf Formale Grammatik (DE-588)4154991-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Formale Grammatik (DE-588)4154991-0 s Inferenz Künstliche Intelligenz (DE-588)4333533-0 s DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018986762&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
spellingShingle | De la Higuera, Colin 1962- Grammatical inference learning automata and grammars Formal languages Logic, Symbolic and mathematical Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd Formale Grammatik (DE-588)4154991-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
subject_GND | (DE-588)4333533-0 (DE-588)4154991-0 (DE-588)4193754-5 |
title | Grammatical inference learning automata and grammars |
title_auth | Grammatical inference learning automata and grammars |
title_exact_search | Grammatical inference learning automata and grammars |
title_full | Grammatical inference learning automata and grammars Colin de la Higuera |
title_fullStr | Grammatical inference learning automata and grammars Colin de la Higuera |
title_full_unstemmed | Grammatical inference learning automata and grammars Colin de la Higuera |
title_short | Grammatical inference |
title_sort | grammatical inference learning automata and grammars |
title_sub | learning automata and grammars |
topic | Formal languages Logic, Symbolic and mathematical Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd Formale Grammatik (DE-588)4154991-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
topic_facet | Formal languages Logic, Symbolic and mathematical Inferenz Künstliche Intelligenz Formale Grammatik Maschinelles Lernen |
url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018986762&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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