Learning with recurrent neural networks:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London u.a.
Springer
2000
|
| Schriftenreihe: | Lecture notes in control and information sciences
254 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | X, 148 S. graph. Darst. |
| ISBN: | 185233343X |
Internformat
MARC
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| 100 | 1 | |a Hammer, Barbara |d 1970- |e Verfasser |0 (DE-588)13377435X |4 aut | |
| 245 | 1 | 0 | |a Learning with recurrent neural networks |c Barbara Hammer |
| 264 | 1 | |a London u.a. |b Springer |c 2000 | |
| 300 | |a X, 148 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Lecture notes in control and information sciences |v 254 | |
| 650 | 7 | |a Neurale netwerken |2 gtt | |
| 650 | 7 | |a Réseaux neuronaux (Informatique) |2 ram | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 0 | 7 | |a NP-vollständiges Problem |0 (DE-588)4138229-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Rekursives neuronales Netz |0 (DE-588)4379549-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 655 | 7 | |8 1\p |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Rekursives neuronales Netz |0 (DE-588)4379549-3 |D s |
| 689 | 1 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 1 | 2 | |a NP-vollständiges Problem |0 (DE-588)4138229-8 |D s |
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Datensatz im Suchindex
| _version_ | 1807322954744201216 |
|---|---|
| adam_text |
TABLE
OF
CONTENTS
1.
INTRODUCTION
.
1
2.
RECURRENT
AND
FOLDING
NETWORKS
.
5
2.1
DEFINITIONS
.
5
2.2
TRAINING
.
11
2.3
BACKGROUND
.
13
2.4
APPLICATIONS
.
15
2.4.1
TERM
CLASSIFICATION
.
15
2.4.2
LEARNING
TREE
AUTOMATA
.
15
2.4.3
CONTROL
OF
SEARCH
HEURISTICS
FOR
AUTOMATED
DEDUCTION
16
2.4.4
CLASSIFICATION
OF
CHEMICAL
DATA
.
16
2.4.5
LOGO
CLASSIFICATION
.
18
3.
APPROXIMATION
ABILITY
.
19
3.1
FOUNDATIONS
.
20
3.2
APPROXIMATION
IN
PROBABILITY
.
25
3.2.1
INTERPOLATION
OF
A
FINITE
SET
OF
DATA
.
25
3.2.2
APPROXIMATION
OF
A
MAPPING
IN
PROBABILITY
.
30
3.2.3
INTERPOLATION
WITH
A
=
H
.
35
3.3
APPROXIMATION
IN
THE
MAXIMUM
NORM
.
36
3.3.1
NEGATIVE
EXAMPLES
.
36
3.3.2
APPROXIMATION
ON
UNARY
SEQUENCES
.
39
3.3.3
NOISY
COMPUTATION
.
44
3.3.4
APPROXIMATION
ON
A
FINITE
TIME
INTERVAL
.
46
3.4
DISCUSSION
AND
OPEN
QUESTIONS
.
48
4.
LEARNABILITY
.
51
4.1
THE
LEARNING
SCENARIO
.
53
4.1.1
DISTRIBUTION-DEPENDENT,
MODEL-DEPENDENT
LEARNING
.
54
4.1.2
DISTRIBUTION-INDEPENDENT,
MODEL-DEPENDENT
LEARNING
.
57
4.1.3
MODEL-FREE
LEARNING
.
58
4.1.4
DEALING
WITH
INFINITE
CAPACITY
.
60
4.1.5
VC-DIMENSION
OF
NEURAL
NETWORKS
.
62
4.2
PAC
LEARNABILITY
.
63
X
TABLE
OF
CONTENTS
4.2.1
DISTRIBUTION-DEPENDENT
LEARNING
.
63
4.2.2
SCALE
SENSITIVE
TERMS
.
66
4.2.3
NOISY
DATA
.
70
4.2.4
MODEL-FREE
LEARNING
.
74
4.2.5
DEALING
WITH
INFINITE
CAPACITY
.
75
4.3
BOUNDS
ON
THE
VC-DIMENSION
OF
FOLDING
NETWORKS
.
79
4.3.1
TECHNICAL
DETAILS
.
79
4.3.2
ESTIMATION
OF
THE
VC-DIMENSION
.
83
4.3.3
LOWER
BOUNDS
ON
FAT
YY
(F)
.
89
4.4
CONSEQUENCES
FOR
LEARNABILITY
.
93
4.5
LOWER
BOUNDS
FOR
THE
LRAAM
.
97
4.6
DISCUSSION
AND
OPEN
QUESTIONS
.
98
5.
COMPLEXITY
.
103
5.1
THE
LOADING
PROBLEM
.
105
5.2
THE
PERCEPTRON
CASE
.
110
5.2.1
POLYNOMIAL
SITUATIONS
.
110
5.2.2
NP-RESULTS
.
113
5.3
THE
SIGMOIDAL
CASE
.
122
5.4
DISCUSSION
AND
OPEN
QUESTIONS
.
130
6.
CONCLUSION
.
133
BIBLIOGRAPHY
.
137
INDEX
.
145 |
| any_adam_object | 1 |
| author | Hammer, Barbara 1970- |
| author_GND | (DE-588)13377435X |
| author_facet | Hammer, Barbara 1970- |
| author_role | aut |
| author_sort | Hammer, Barbara 1970- |
| author_variant | b h bh |
| building | Verbundindex |
| bvnumber | BV013153244 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.87. |
| callnumber-search | QA76.87. |
| callnumber-sort | QA 276.87 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | SI 845 |
| ctrlnum | (OCoLC)44013868 (DE-599)BVBBV013153244 |
| dewey-full | 006.3/2 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/2 |
| dewey-search | 006.3/2 |
| dewey-sort | 16.3 12 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| format | Book |
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| genre_facet | Hochschulschrift |
| id | DE-604.BV013153244 |
| illustrated | Illustrated |
| indexdate | 2024-08-14T01:04:42Z |
| institution | BVB |
| isbn | 185233343X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008961953 |
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| open_access_boolean | |
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| physical | X, 148 S. graph. Darst. |
| publishDate | 2000 |
| publishDateSearch | 2000 |
| publishDateSort | 2000 |
| publisher | Springer |
| record_format | marc |
| series | Lecture notes in control and information sciences |
| series2 | Lecture notes in control and information sciences |
| spelling | Hammer, Barbara 1970- Verfasser (DE-588)13377435X aut Learning with recurrent neural networks Barbara Hammer London u.a. Springer 2000 X, 148 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Lecture notes in control and information sciences 254 Neurale netwerken gtt Réseaux neuronaux (Informatique) ram Neural networks (Computer science) NP-vollständiges Problem (DE-588)4138229-8 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Rekursives neuronales Netz (DE-588)4379549-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Neuronales Netz (DE-588)4226127-2 s DE-604 Rekursives neuronales Netz (DE-588)4379549-3 s NP-vollständiges Problem (DE-588)4138229-8 s Lecture notes in control and information sciences 254 (DE-604)BV005848579 254 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008961953&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hammer, Barbara 1970- Learning with recurrent neural networks Lecture notes in control and information sciences Neurale netwerken gtt Réseaux neuronaux (Informatique) ram Neural networks (Computer science) NP-vollständiges Problem (DE-588)4138229-8 gnd Neuronales Netz (DE-588)4226127-2 gnd Rekursives neuronales Netz (DE-588)4379549-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4138229-8 (DE-588)4226127-2 (DE-588)4379549-3 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Learning with recurrent neural networks |
| title_auth | Learning with recurrent neural networks |
| title_exact_search | Learning with recurrent neural networks |
| title_full | Learning with recurrent neural networks Barbara Hammer |
| title_fullStr | Learning with recurrent neural networks Barbara Hammer |
| title_full_unstemmed | Learning with recurrent neural networks Barbara Hammer |
| title_short | Learning with recurrent neural networks |
| title_sort | learning with recurrent neural networks |
| topic | Neurale netwerken gtt Réseaux neuronaux (Informatique) ram Neural networks (Computer science) NP-vollständiges Problem (DE-588)4138229-8 gnd Neuronales Netz (DE-588)4226127-2 gnd Rekursives neuronales Netz (DE-588)4379549-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Neurale netwerken Réseaux neuronaux (Informatique) Neural networks (Computer science) NP-vollständiges Problem Neuronales Netz Rekursives neuronales Netz Maschinelles Lernen Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008961953&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV005848579 |
| work_keys_str_mv | AT hammerbarbara learningwithrecurrentneuralnetworks |