Error estimation and model selection:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | German |
| Veröffentlicht: |
Sankt Augustin
Infix
1999
|
| Schriftenreihe: | Dissertationen zur künstlichen Intelligenz
225 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XII, 126 S. |
| ISBN: | 3896012258 |
Internformat
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV012932274 | ||
| 003 | DE-604 | ||
| 005 | 20150325 | ||
| 007 | t | ||
| 008 | 991221s1999 gw m||| 00||| ger d | ||
| 016 | 7 | |a 958115346 |2 DE-101 | |
| 020 | |a 3896012258 |c kart. : DM 48.00, sfr 44.50, S 350.00 |9 3-89601-225-8 | ||
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| 084 | |a MAT 661d |2 stub | ||
| 100 | 1 | |a Scheffer, Tobias |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Error estimation and model selection |c Tobias Scheffer |
| 264 | 1 | |a Sankt Augustin |b Infix |c 1999 | |
| 300 | |a XII, 126 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Dissertationen zur künstlichen Intelligenz |v 225 | |
| 502 | |a Zugl.: Berlin ,Techn. Univ., Diss., 1999 | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 830 | 0 | |a Dissertationen zur künstlichen Intelligenz |v 225 |w (DE-604)BV005345280 |9 225 | |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008805794&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-008805794 | |
Datensatz im Suchindex
| _version_ | 1807504894706319360 |
|---|---|
| adam_text |
CONTENTS
1
INTRODUCTION
1
1.1
MACHINE
LEARNING
.
1
1.2
THE
NEED
FOR
BIAS
IN
LEARNING
.
3
1.3
MODEL
SELECTION
.
5
1.4
APPLICATIONS
OF
MACHINE
LEARNING
.
8
1.5
PRINCIPLE
CONTRIBUTIONS
.
9
1.6
ORGANIZATION
.
10
2
PRELIMINARIES
11
2.1
TERMINOLOGY
USED
THROUGHOUT
THE
BOOK
.
11
2.2
MODELS
OF
GENERALIZATION
.
13
2.2.1
GOLD
'
S
FRAMEWORK
OF
LEARNING
.
13
2.2.2
THE
PAC
AND
VC
MODELS
OF
GENERALIZATION
.
14
2.2.3
THE
RELATIONSHIP
BETWEEN
PAC
AND
GOLD
'
S
FRAMEWORK
.
16
2.2.4
THE
BAYESIAN
FRAMEWORK
.
18
2.2.5
LINKS
BETWEEN
PAC/VC
AND
BAYESIAN
LEARNING
.
20
2.3
NO
FREE
LUNCH
.
21
2.4
MODEL
SELECTION
.
22
2.4.1
OCCAM
ALGORITHMS
.
23
2.4.2
COMPLEXITY
PENALIZATION
.
25
2.4.3
CROSS
VALIDATION
.
26
2.5
EMPIRICAL
METHODOLOGY
OF
MACHINE
LEARNING
.
29
2.6
SUMMARY
.
30
3
EXPECTED
ERROR
ANALYSIS
32
3.1
OVERVIEW
ON
THE
FRAMEWORK
.
33
3.2
SOLUTION
FOR
INDEPENDENT
ERROR
VALUES
.
34
3.3
GENERAL
SOLUTION
.
36
3.4
ESTIMATINGP
[/L
}(E
JD
(/I)|7F
1
,DXY)
.
38
3.5
WHAT
IS
OVER-FITTING?
.
40
3.6
ROBUSTNESS
AGAINST
INACCURATE
ESTIMATES
OF
|
HI
|
.
43
3.7
EMPIRICAL
STUDIES
.
43
3.7.1
ARTIFICIAL
PROBLEM
.
43
X
CONTENTS
3.7.2
LEARNING
BOOLEAN
DECISION
TREES
.
46
3.8
SCALING
UP:
TEXT
CATEGORIZATION
.
48
3.9
DISCUSSION
.
55
3.10
SUMMARY
.
57
4
ASSUMPTIONS
THAT
JUSTIFY
MODEL
SELECTION
59
4.1
BOUNDS
ON
THE
PERFORMANCE
OF
MODEL
SELECTION
.
60
4.2
OCCAM
ALGORITHMS
.
61
4.3
CROSS
VALIDATION
.
65
4.4
CASE
STUDY:
BOOLEAN
FUNCTIONS
.
67
4.5
DISCUSSION
AND
RELATED
RESULTS
.
68
4.6
SUMMARY
.
71
5
ASSESSMENT
OF
LEARNING
ALGORITHMS
73
5.1
CHEMOFF
BOUNDS
.
74
5.2
INFORMATION-THEORETIC
APPROACH
.
75
5.2.1
PARAMETER
ADJUSTMENT
.
76
5.3
ONE-SHOT
TRAINING
AND
TEST
.
77
5.3.1
AFFECTED
BENCHMARK
PROBLEMS
.
78
5.4
N-FOLD
CROSS
VALIDATION
WITH
PARAMETER
ADJUSTMENT
.
79
5.4.1
AFFECTED
BENCHMARK
PROBLEMS
.
79
5.5
N-FOLD
CROSS
VALIDATION
WITH
FIXED
PARAMETERS
.
80
5.5.1
AFFECTED
BENCHMARK
PROBLEMS
.
81
5.6
ALMOST
UNBIASED
ASSESSMENT
.
82
5.7
DISCUSSION
.
83
5.8
SUMMARY
.
84
6
COMPLEXITY
ISSUES
85
6.1
BOOSTING
.
85
6.2
FURTHER
DEFINITIONS
.
87
6.3
A
WORST-CASE
BOUND
FOR
ADABOOST
WITH
PERCEPTRONS
.
88
6.4
BOOSTING
DECISION
STUMPS
.
91
6.5
DISCUSSION
AND
RELATED
WORK
.
92
6.6
SUMMARY
.
93
7
CONCLUSION
94
7.1
EXPECTED
ERROR
ANALYSIS
.
95
7.2 IS
THE
ERROR
RATE
A
PROPERTY
OF
THE
HYPOTHESIS
LANGUAGE?
.
97
7.3
WHEN
DOES
MODEL
SELECTION
WORK?
.
98
7.4
APPLICABILITY
OF
LEARNING
ALGORITHMS
.
100
APPENDIX
A
PROOF
OF
THEOREM
5
.
101
CONTENTS
XI
B
EFFICIENT
IMPLEMENTATION
OF
THEOREM
5
.
103
C
PROOF
OF
THEOREM
6
.
104
D
PROOF
OF
THEOREM
7
.
105
E
EFFICIENT
IMPLEMENTATION
OF
THEOREM
7
.
106
F
PROOF
OF
THEOREM
8
.
107
G
EXPECTED
LEARNING
CURVE
FOR
BOOLEAN
FUNCTIONS
.
108
G.
1
BOOLEAN
FUNCTIONS
OVER
ATTRIBUTES
XI
,.,
108
G.2
EXPECTED
LEARNING
CURVE
FOR
BOOLEAN
FUNCTIONS
OVER
I
ATTRIBUTES
.
110
H
NOTATION
.
112
BIBLIOGRAPHY
114 |
| any_adam_object | 1 |
| author | Scheffer, Tobias |
| author_facet | Scheffer, Tobias |
| author_role | aut |
| author_sort | Scheffer, Tobias |
| author_variant | t s ts |
| building | Verbundindex |
| bvnumber | BV012932274 |
| classification_tum | MAT 661d |
| ctrlnum | (OCoLC)45542042 (DE-599)BVBBV012932274 |
| discipline | Mathematik |
| format | Thesis Book |
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| genre_facet | Hochschulschrift |
| id | DE-604.BV012932274 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-16T01:16:33Z |
| institution | BVB |
| isbn | 3896012258 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008805794 |
| oclc_num | 45542042 |
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| owner_facet | DE-91G DE-BY-TUM DE-29T DE-703 DE-83 |
| physical | XII, 126 S. |
| publishDate | 1999 |
| publishDateSearch | 1999 |
| publishDateSort | 1999 |
| publisher | Infix |
| record_format | marc |
| series | Dissertationen zur künstlichen Intelligenz |
| series2 | Dissertationen zur künstlichen Intelligenz |
| spelling | Scheffer, Tobias Verfasser aut Error estimation and model selection Tobias Scheffer Sankt Augustin Infix 1999 XII, 126 S. txt rdacontent n rdamedia nc rdacarrier Dissertationen zur künstlichen Intelligenz 225 Zugl.: Berlin ,Techn. Univ., Diss., 1999 (DE-588)4113937-9 Hochschulschrift gnd-content Dissertationen zur künstlichen Intelligenz 225 (DE-604)BV005345280 225 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008805794&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Scheffer, Tobias Error estimation and model selection Dissertationen zur künstlichen Intelligenz |
| subject_GND | (DE-588)4113937-9 |
| title | Error estimation and model selection |
| title_auth | Error estimation and model selection |
| title_exact_search | Error estimation and model selection |
| title_full | Error estimation and model selection Tobias Scheffer |
| title_fullStr | Error estimation and model selection Tobias Scheffer |
| title_full_unstemmed | Error estimation and model selection Tobias Scheffer |
| title_short | Error estimation and model selection |
| title_sort | error estimation and model selection |
| topic_facet | Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008805794&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV005345280 |
| work_keys_str_mv | AT scheffertobias errorestimationandmodelselection |