000 | 03777nam a22005895i 4500 | ||
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001 | 978-3-540-45435-9 | ||
003 | DE-He213 | ||
005 | 20240423132542.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s2002 gw | s |||| 0|eng d | ||
020 |
_a9783540454359 _9978-3-540-45435-9 |
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024 | 7 |
_a10.1007/3-540-45435-7 _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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_aComputational Learning Theory _h[electronic resource] : _b15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings / _cedited by Jyrki Kivinen, Robert H. Sloan. |
250 | _a1st ed. 2002. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2002. |
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300 |
_aXII, 412 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 ; _v2375 |
|
505 | 0 | _aStatistical Learning Theory -- Agnostic Learning Nonconvex Function Classes -- Entropy, Combinatorial Dimensions and Random Averages -- Geometric Parameters of Kernel Machines -- Localized Rademacher Complexities -- Some Local Measures of Complexity of Convex Hulls and Generalization Bounds -- Online Learning -- Path Kernels and Multiplicative Updates -- Predictive Complexity and Information -- Mixability and the Existence of Weak Complexities -- A Second-Order Perceptron Algorithm -- Tracking Linear-Threshold Concepts with Winnow -- Inductive Inference -- Learning Tree Languages from Text -- Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data -- Inferring Deterministic Linear Languages -- Merging Uniform Inductive Learners -- The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions -- PAC Learning -- New Lower Bounds for Statistical Query Learning -- Exploring Learnability between Exact and PAC -- PAC Bounds for Multi-armed Bandit and Markov Decision Processes -- Bounds for the Minimum Disagreement Problem with Applications to Learning Theory -- On the Proper Learning of Axis Parallel Concepts -- Boosting -- A Consistent Strategy for Boosting Algorithms -- The Consistency of Greedy Algorithms for Classification -- Maximizing the Margin with Boosting -- Other Learning Paradigms -- Performance Guarantees for Hierarchical Clustering -- Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures -- Prediction and Dimension -- Invited Talk -- Learning the Internet. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aMachine theory. | |
650 | 0 | _aComputer science. | |
650 | 0 | _aAlgorithms. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aFormal Languages and Automata Theory. |
650 | 2 | 4 | _aTheory of Computation. |
650 | 2 | 4 | _aAlgorithms. |
700 | 1 |
_aKivinen, Jyrki. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aSloan, Robert H. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783540438366 |
776 | 0 | 8 |
_iPrinted edition: _z9783662167175 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v2375 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/3-540-45435-7 |
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912 | _aZDB-2-LNC | ||
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942 | _cSPRINGER | ||
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