01225nam0 2200265 450 00003566220140114091125.020131211d1953----km-y0itaa50------baitaIT<<Il>> sistema colturale Del Pelo Pardiillustrazione elementare per i lavoratori dei campi, tratta dalle lezioni svolte nei Corsi per contadiniTommaso Del Pelo Pardia cura della Sezione per la attività dimostrativa del sistema colturale Del Pelo Pardi, presso la Stazione chimico-agraria sperimentale di Roma[S.l.s.n.]1953164 p.ill.31 cm.Terreni agrari631.4(22. ed.)PedologiaDel Pelo Pardi,Tommaso446493ITUniversità della Basilicata - B.I.A.REICATunimarc000035662Sistema colturale Del Pelo Pardi101467UNIBASSTD0930120131211BAS011422TTM3020140114BAS010907TTM3020140114BAS010911BAS01BAS01BOOKBASA2Polo Tecnico-ScientificoFVIGFondo ViggianiFVig/4103841038T41038Collocato presso la Scuola di Agraria2013121135Stanza riservata05343nam 22006495 450 99646605440331620200706005738.03-540-48096-X10.1007/3-540-57370-4(CKB)1000000000234045(SSID)ssj0000321182(PQKBManifestationID)11260282(PQKBTitleCode)TC0000321182(PQKBWorkID)10276823(PQKB)11374565(DE-He213)978-3-540-48096-9(PPN)155229028(EXLCZ)99100000000023404520121227d1993 u| 0engurnn|008mamaatxtccrAlgorithmic Learning Theory[electronic resource] 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings /edited by Klaus P. Jantke, Shigenobu Kobayashi, Etsuji Tomita, Takashi Yokomori1st ed. 1993.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,1993.1 online resource (XI, 428 p.) Lecture Notes in Artificial Intelligence ;744Bibliographic Level Mode of Issuance: Monograph3-540-57370-4 Identifying and using patterns in sequential data -- Learning theory toward Genome Informatics -- Optimal layered learning: A PAC approach to incremental sampling -- Reformulation of explanation by linear logic toward logic for explanation -- Towards efficient inductive synthesis of expressions from input/output examples -- A typed ?-calculus for proving-by-example and bottom-up generalization procedure -- Case-based representation and learning of pattern languages -- Inductive resolution -- Generalized unification as background knowledge in learning logic programs -- Inductive inference machines that can refute hypothesis spaces -- On the duality between mechanistic learners and what it is they learn -- On aggregating teams of learning machines -- Learning with growing quality -- Use of reduction arguments in determining Popperian FIN-type learning capabilities -- Properties of language classes with finite elasticity -- Uniform characterizations of various kinds of language learning -- How to invent characterizable inference methods for regular languages -- Neural Discriminant Analysis -- A new algorithm for automatic configuration of Hidden Markov Models -- On the VC-dimension of depth four threshold circuits and the complexity of Boolean-valued functions -- On the sample complexity of consistent learning with one-sided error -- Complexity of computing Vapnik-Chervonenkis dimension -- ?-approximations of k-label spaces -- Exact learning of linear combinations of monotone terms from function value queries -- Thue systems and DNA — A learning algorithm for a subclass -- The VC-dimensions of finite automata with n states -- Unifying learning methods by colored digraphs -- A perceptual criterion for visually controlling learning -- Learning strategies using decision lists -- A decomposition based induction model for discovering concept clusters from databases -- Algebraic structure of some learning systems -- Induction of probabilistic rules based on rough set theory.This volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. In addition to 3 invited papers, 29 papers were selected from 47 submitted extended abstracts. The workshop was the fourth in a series of ALT workshops, whose focus is on theories of machine learning and the application of such theories to real-world learning problems. The ALT workshops have been held annually since 1990, sponsored by the Japanese Society for Artificial Intelligence. The volume is organized into parts on inductive logic and inference, inductive inference, approximate learning, query learning, explanation-based learning, and new learning paradigms.Lecture Notes in Artificial Intelligence ;744Artificial intelligenceMathematicsComputersArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Mathematics, generalhttps://scigraph.springernature.com/ontologies/product-market-codes/M00009Theory of Computationhttps://scigraph.springernature.com/ontologies/product-market-codes/I16005Computation by Abstract Deviceshttps://scigraph.springernature.com/ontologies/product-market-codes/I16013Artificial intelligence.Mathematics.Computers.Artificial Intelligence.Mathematics, general.Theory of Computation.Computation by Abstract Devices.006.3Jantke Klaus Pedthttp://id.loc.gov/vocabulary/relators/edtKobayashi Shigenobuedthttp://id.loc.gov/vocabulary/relators/edtTomita Etsujiedthttp://id.loc.gov/vocabulary/relators/edtYokomori Takashiedthttp://id.loc.gov/vocabulary/relators/edtBOOK996466054403316Algorithmic Learning Theory771965UNISA