01085nam0-22003731i-450-99000067820040332120090720100400.0000067820FED01000067820(Aleph)000067820FED0100006782020020821d1980----km-y0itay50------baitafrea---a---001yy<<L'>>isolamento termicoguida al risparmio energetico nelle abitazioniMichel Frenot e Nabih Sawayatraduzionedi Paolo CellaMilanoLonganesi1980191 p.ill.23 cm<<La >>vostra via155EdificiIsolamento termico693.8Frenot,Michel2295Cella,PaoloSawaya,NabihITUNINARICAUNIMARCBK99000067820040332108 C 2096801DINED13 D 43 0232429FINBC01 H 304405017DINSTDINSTFINBCDINEDIsolamento termico325368UNINA03817oam 2200625 450 99646561410331620210722144650.01-280-80456-497866108045663-540-46769-610.1007/3-540-46769-6(CKB)1000000000211187(EBL)3036569(SSID)ssj0000288523(PQKBManifestationID)11231401(PQKBTitleCode)TC0000288523(PQKBWorkID)10381275(PQKB)10162948(DE-He213)978-3-540-46769-4(MiAaPQ)EBC3036569(MiAaPQ)EBC6489632(PPN)155189530(EXLCZ)99100000000021118720210722d1999 uy 0engur|n|---|||||txtccrAlgorithmic learning theory 10th International Conference, ALT'99, Tokyo, Japan, December 6-8, 1999 : proceedings /Osamu Watanabe, Takashi Yokomori, eds1st ed. 1999.Berlin, Germany ;New York, New York :Springer,[1999]©19991 online resource (374 p.)Lecture Notes in Artificial Intelligence ;1720Description based upon print version of record.3-540-66748-2 Includes bibliographical references and index.Invited 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 Unions of Pattern Languages and Tree Patterns.Lecture Notes in Artificial Intelligence ;1720Machine learningCongressesComputer algorithmsCongressesMachine learningComputer algorithms006.3/1Watanabe Osamu1958-Yokomori TakashiALT'99MiAaPQMiAaPQUtOrBLWBOOK996465614103316Algorithmic Learning Theory771965UNISA