000 | 03931nam a22005655i 4500 | ||
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001 | 978-3-540-46769-4 | ||
003 | DE-He213 | ||
005 | 20240423132437.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s1999 gw | s |||| 0|eng d | ||
020 |
_a9783540467694 _9978-3-540-46769-4 |
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024 | 7 |
_a10.1007/3-540-46769-6 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
245 | 1 | 0 |
_aAlgorithmic Learning Theory _h[electronic resource] : _b10th International Conference, ALT '99 Tokyo, Japan, December 6-8, 1999 Proceedings / _cedited by Osamu Watanabe, Takashi Yokomori. |
250 | _a1st ed. 1999. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c1999. |
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300 |
_aXII, 372 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v1720 |
|
505 | 0 | _aInvited Lectures -- Tailoring Representations to Different Requirements -- Theoretical Views of Boosting and Applications -- Extended Stochastic Complexity and Minimax Relative Loss Analysis -- Regular Contributions -- Algebraic Analysis for Singular Statistical Estimation -- Generalization Error of Linear Neural Networks in Unidentifiable Cases -- The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa -- The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract) -- The VC-Dimension of Subclasses of Pattern Languages -- On the V ? Dimension for Regression in Reproducing Kernel Hilbert Spaces -- On the Strength of Incremental Learning -- Learning from Random Text -- Inductive Learning with Corroboration -- Flattening and Implication -- Induction of Logic Programs Based on ?-Terms -- Complexity in the Case Against Accuracy: When Building One Function-Free Horn Clause Is as Hard as Any -- A Method of Similarity-Driven Knowledge Revision for Type Specializations -- PAC Learning with Nasty Noise -- Positive and Unlabeled Examples Help Learning -- Learning Real Polynomials with a Turing Machine -- Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E3 Algorithm -- A Note on Support Vector Machine Degeneracy -- Learnability of Enumerable Classes of Recursive Functions from “Typical” Examples -- On the Uniform Learnability of Approximations to Non-recursive Functions -- Learning Minimal Covers of Functional Dependencies with Queries -- Boolean Formulas Are Hard to Learn for Most Gate Bases -- Finding Relevant Variables in PAC Model with Membership Queries -- General Linear Relations among Different Types of Predictive Complexity -- Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph -- On Learning Unionsof Pattern Languages and Tree Patterns. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aMachine theory. | |
650 | 0 | _aAlgorithms. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aFormal Languages and Automata Theory. |
650 | 2 | 4 | _aAlgorithms. |
700 | 1 |
_aWatanabe, Osamu. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aYokomori, Takashi. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783540667483 |
776 | 0 | 8 |
_iPrinted edition: _z9783662165096 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v1720 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/3-540-46769-6 |
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912 | _aZDB-2-SXCS | ||
912 | _aZDB-2-LNC | ||
912 | _aZDB-2-BAE | ||
942 | _cSPRINGER | ||
999 |
_c187820 _d187820 |