Algorithmic learning theory:
Gespeichert in:
Format: | Buch |
---|---|
Sprache: | English |
Veröffentlicht: |
Tokyo
Ohmsha u.a.
1990
|
Schlagworte: | |
Online-Zugang: | Inhaltsverzeichnis |
Beschreibung: | Literaturangaben |
Beschreibung: | XI, 441 S. graph. Darst. |
ISBN: | 3540196617 0387196617 4274076164 |
Internformat
MARC
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245 | 1 | 0 | |a Algorithmic learning theory |c ed. by S. Arikawa ... |
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300 | |a XI, 441 S. |b graph. Darst. | ||
336 | |b txt |2 rdacontent | ||
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Datensatz im Suchindex
_version_ | 1820868136646213632 |
---|---|
adam_text |
TABLE
OF
CONTENTS
INVITED PAPERS
S.
Amari
Mathematical Theory of Neural Learning
D.
Haussler
Decision Theoretic Generalizations of the
PAC
Learning Model
. 21
S. Muggleton
Inductive Logic Programming
. 42
SELECTED PAPERS
[Neural Networks]
A. Namatame
Structured Neural Networks and their Flasli Learning
. 67
R.Oka
A Self-Organizing Network Composed of Symbol Nodes with Location Parameter
. 81
[Concept Formation and Recognition]
B.Shekar, M.Narasimha Murty, G.Krishna
The Function-Acquisition Paradigm in a Knowledge-Based Concept-Synthesis
Environment
. 9
ι
V.Gusev,
N.
Chuzhanova
The Algorithms of Recognition of the Functional Sites
m
Genetic Texts
. . . 109
T. Unemi
On Inductive Learning for Three Kinds of Data Structures
. 120
R.
Orillara,
A.Osuga.Y.Kusui
On Paraphi-asing-Bascd Analogical Reasoning
—
as a Theoretical Base of the
Abduction Support System
. 134
[Analogical Reasoning]
M.
Harao
Analogical Reasoning Based on Higher-Order
L
■nification
.
if>!
J.
Arima
Analogy by
Sinndation —
a Weak Justification Method
. :'··
B. Indurkhya
On the Role of Interpretive Analogy in Ltarning
. 1.1
[Approximate Learning
]
Y. Sakakibara
Occam Algorithms for Learning from Noisy Examples
. 193
J. Kivinen
Reliable and Useful Learning with Uniform Probability Distributions
. ■ . 209
N.
Abe
Learning Commutative Deterministic Finite State Automata in Polynomial
Time
.
223
N.Cesa-Bianchi
Learning the Distribution in the Extended
PAC
Model
. 236
A. Shinohara, S.Miyano
Teachability in Computational Learning
. 247
M.Fulk, S.Jain
Approximate Inference and Scientific Method
. 256
[Inductive Inference
]
K.Jantke
Monotonie
and Non-Monotonic Inductive Inference
. 269
J.Case, S.Jain, A.Sharma
Anomalous Learning Helps Succinctness
. 282
S.Lange, R.
Wiehagen
Polynomial-Time Inference of Pattern Languages
. 289
Y. Takada
Learning Equal Matrix Grammars and Multitape Automata with Structural
Information
. 302
Y
.Takada, K.Hiraishi, Y.Sakakibara
Exact Learning of
Semilinear
Sets
. 314
P.Garcia,
E.Vidal,
J.Oncina
Learning Locally Testable Languages in the Strict Sense
. 325
T.Shinohara
Inductive Inference of
Monotonie
Formal Systems from Positive Data
. ■ ■ 339
[New Learning Paradigms
]
S.Liu, M.Hagiya
Model Inference of Constrained Recursive Figures
. 355
S.Muggleton, C.Feng
Efficient Induction of Logic Programs
. 368
T.Tanaka
Deciding What to Learn in Explanation-Based Macro-Rule Learning
. . . 382
M.Hagiya
Synthesis of Rewrite Programs by Higher-Order and Semantic
Unification
. 396
A.Togashi, S.Noguchi
Inductive Inference of Term Rewriting Systems Realizing Algebras
. 411
P.Laird, E.Gamble
EBG and Term-Rewriting Systems
. 425
Index of Authors
. 441 |
any_adam_object | 1 |
author_GND | (DE-588)121451089 |
building | Verbundindex |
bvnumber | BV004581249 |
callnumber-first | Q - Science |
callnumber-label | QA274 |
callnumber-raw | QA274.6 |
callnumber-search | QA274.6 |
callnumber-sort | QA 3274.6 |
callnumber-subject | QA - Mathematics |
classification_rvk | SS 1990 |
classification_tum | DAT 708f |
ctrlnum | (OCoLC)23744497 (DE-599)BVBBV004581249 |
dewey-full | 006.31 |
dewey-hundreds | 000 - Computer science, information, general works |
dewey-ones | 006 - Special computer methods |
dewey-raw | 006.31 |
dewey-search | 006.31 |
dewey-sort | 16.31 |
dewey-tens | 000 - Computer science, information, general works |
discipline | Informatik |
format | Book |
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genre_facet | Konferenzschrift 1990 Tokio |
id | DE-604.BV004581249 |
illustrated | Illustrated |
indexdate | 2025-01-10T13:19:35Z |
institution | BVB |
isbn | 3540196617 0387196617 4274076164 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-002816992 |
oclc_num | 23744497 |
open_access_boolean | |
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owner_facet | DE-12 DE-91G DE-BY-TUM DE-384 DE-19 DE-BY-UBM DE-739 DE-706 DE-11 DE-188 |
physical | XI, 441 S. graph. Darst. |
publishDate | 1990 |
publishDateSearch | 1990 |
publishDateSort | 1990 |
publisher | Ohmsha u.a. |
record_format | marc |
spelling | Algorithmic learning theory ed. by S. Arikawa ... Tokyo Ohmsha u.a. 1990 XI, 441 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturangaben Learning models (Stochastic processes) Congresses Mathematische Lerntheorie (DE-588)4169103-9 gnd rswk-swf Lerntheorie (DE-588)4114402-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 1990 Tokio gnd-content Mathematische Lerntheorie (DE-588)4169103-9 s DE-604 Maschinelles Lernen (DE-588)4193754-5 s Lerntheorie (DE-588)4114402-8 s Arikawa, Setsuo 1941- Sonstige (DE-588)121451089 oth Digitalisierung TU Muenchen application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=002816992&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
spellingShingle | Algorithmic learning theory Learning models (Stochastic processes) Congresses Mathematische Lerntheorie (DE-588)4169103-9 gnd Lerntheorie (DE-588)4114402-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
subject_GND | (DE-588)4169103-9 (DE-588)4114402-8 (DE-588)4193754-5 (DE-588)1071861417 |
title | Algorithmic learning theory |
title_auth | Algorithmic learning theory |
title_exact_search | Algorithmic learning theory |
title_full | Algorithmic learning theory ed. by S. Arikawa ... |
title_fullStr | Algorithmic learning theory ed. by S. Arikawa ... |
title_full_unstemmed | Algorithmic learning theory ed. by S. Arikawa ... |
title_short | Algorithmic learning theory |
title_sort | algorithmic learning theory |
topic | Learning models (Stochastic processes) Congresses Mathematische Lerntheorie (DE-588)4169103-9 gnd Lerntheorie (DE-588)4114402-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
topic_facet | Learning models (Stochastic processes) Congresses Mathematische Lerntheorie Lerntheorie Maschinelles Lernen Konferenzschrift 1990 Tokio |
url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=002816992&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
work_keys_str_mv | AT arikawasetsuo algorithmiclearningtheory |