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020 _a9783540396246
_9978-3-540-39624-6
024 7 _a10.1007/b14273
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aAlgorithmic Learning Theory
_h[electronic resource] :
_b14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings /
_cedited by Ricard Gavaldà, Klaus P. Jantke, Eiji Takimoto.
250 _a1st ed. 2003.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2003.
300 _aXII, 320 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2842
505 0 _aInvited Papers -- Abduction and the Dualization Problem -- Signal Extraction and Knowledge Discovery Based on Statistical Modeling -- Association Computation for Information Access -- Efficient Data Representations That Preserve Information -- Can Learning in the Limit Be Done Efficiently? -- Inductive Inference -- Intrinsic Complexity of Uniform Learning -- On Ordinal VC-Dimension and Some Notions of Complexity -- Learning of Erasing Primitive Formal Systems from Positive Examples -- Changing the Inference Type – Keeping the Hypothesis Space -- Learning and Information Extraction -- Robust Inference of Relevant Attributes -- Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables -- Learning with Queries -- On the Learnability of Erasing Pattern Languages in the Query Model -- Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries -- Learning with Non-linear Optimization -- Kernel Trick Embedded Gaussian Mixture Model -- Efficiently Learning the Metric with Side-Information -- Learning Continuous Latent Variable Models with Bregman Divergences -- A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation -- Learning from Random Examples -- On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays -- Learning a Subclass of Regular Patterns in Polynomial Time -- Identification with Probability One of Stochastic Deterministic Linear Languages -- Online Prediction -- Criterion of Calibration for Transductive Confidence Machine with Limited Feedback -- Well-Calibrated Predictions from Online Compression Models -- Transductive Confidence Machine Is Universal -- On the Existence and Convergence of Computable Universal Priors.
650 0 _aArtificial intelligence.
650 0 _aComputer science.
650 0 _aAlgorithms.
650 0 _aMachine theory.
650 0 _aNatural language processing (Computer science).
650 1 4 _aArtificial Intelligence.
650 2 4 _aTheory of Computation.
650 2 4 _aAlgorithms.
650 2 4 _aFormal Languages and Automata Theory.
650 2 4 _aNatural Language Processing (NLP).
700 1 _aGavaldà, Ricard.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aJantke, Klaus P.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aTakimoto, Eiji.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540202912
776 0 8 _iPrinted edition:
_z9783662163023
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2842
856 4 0 _uhttps://doi.org/10.1007/b14273
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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