LEADER 01406cam0-22005051i-450- 001 990006563410403321 005 20141002114217.0 035 $a000656341 035 $aFED01000656341 035 $a(Aleph)000656341FED01 035 $a000656341 100 $a20010426d1983----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $a<>politica industriale della CEE$edocumentazione generale e settoriale 210 $aRoma$cCamera dei deputati$d1983 215 $a371 p.$d21 cm 225 1 $aQuaderni di documentazione$v5 300 $aIn testa al front.: Camera dei deputati 676 $a340.3 676 $a337.1 710 02$aComunità europea$0342995 712 01$aItalia.$bCamera dei deputati 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006563410403321 952 $aDI VIII 371$b1274$fDEC 952 $aDT XIV-331$b590/a$fDEC 952 $aDI 8/371$b1274$fDEC 952 $aL5.71$b8349$fDECTS 952 $aXXX COD. 372 (5)$b24282$fFSPBC 952 $aIII Q 200$b26645$fFSPBC 952 $a3-F-6$b4033$fDDCP 952 $aXV C 200 (5)$b1100*$fFGBC 952 $aSE 011.07.15-$b672$fDECSE 959 $aDEC 959 $aDEC 959 $aDECTS 959 $aFSPBC 959 $aDDCP 959 $aFGBC 959 $aDECSE 996 $aPolitica industriale della CEE$9624189 997 $aUNINA LEADER 04425nam 22005655 450 001 996465872103316 005 20200704221759.0 010 $a3-540-70719-0 024 7 $a10.1007/3-540-61863-5 035 $a(CKB)1000000000234551 035 $a(SSID)ssj0000321179 035 $a(PQKBManifestationID)11238202 035 $a(PQKBTitleCode)TC0000321179 035 $a(PQKBWorkID)10262818 035 $a(PQKB)10119055 035 $a(DE-He213)978-3-540-70719-6 035 $a(PPN)15523448X 035 $a(EXLCZ)991000000000234551 100 $a20121227d1996 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAlgorithmic Learning Theory$b[electronic resource] $e7th International Workshop, ALT '96, Sydney, Australia, October 23 - 25, 1996. Proceedings /$fedited by Setsuo Arikawa, Arun K. Sharma 205 $a1st ed. 1996. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1996. 215 $a1 online resource (XVII, 337 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v1160 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-61863-5 327 $aManaging complexity in neuroidal circuits -- Learnability of exclusive-or expansion based on monotone DNF formulas -- Improved bounds about on-line learning of smooth functions of a single variable -- Query learning of bounded-width OBDDs -- Learning a representation for optimizable formulas -- Limits of exact algorithms for inference of minimum size finite state machines -- Genetic fitness optimization using rapidly mixing Markov chains -- The kindest cut: Minimum message length segmentation -- Reducing complexity of decision trees with two variable tests -- The complexity of exactly learning algebraic concepts -- Efficient learning of real time two-counter automata -- Cost-sensitive feature reduction applied to a hybrid genetic algorithm -- Effects of Feature Selection with ?Blurring? on neurofuzzy systems -- Boosting first-order learning -- Incorporating hypothetical knowledge into the process of inductive synthesis -- Induction of Constraint Logic Programs -- Constructive learning of translations based on dictionaries -- Inductive logic programming beyond logical implication -- Noise elimination in inductive concept learning: A case study in medical diagnosis -- MML estimation of the parameters of the spherical fisher distribution -- Learning by erasing -- On learning and co-learning of minimal programs -- Inductive inference of unbounded unions of pattern languages from positive data -- A class of prolog programs inferable from positive data -- Vacillatory and BC learning on noisy data -- Transformations that preserve learnability -- Probabilistic limit identification up to ?small? sets -- Reflecting inductive inference machines and its improvement by therapy. 330 $aThis book constitutes the refereed proceedings of the 7th International Workshop on Algorithmic Learning Theory, ALT '96, held in Sydney, Australia, in October 1996. The 16 revised full papers presented were selected from 41 submissions; also included are eight short papers as well as four full length invited contributions by Ross Quinlan, Takeshi Shinohara, Leslie Valiant, and Paul Vitanyi, and an introduction by the volume editors. The book covers all areas related to algorithmic learning theory, ranging from theoretical foundations of machine learning to applications in several areas. 410 0$aLecture Notes in Artificial Intelligence ;$v1160 606 $aArtificial intelligence 606 $aMathematical logic 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 615 0$aArtificial intelligence. 615 0$aMathematical logic. 615 14$aArtificial Intelligence. 615 24$aMathematical Logic and Formal Languages. 676 $a006.3/1 702 $aArikawa$b Setsuo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSharma$b Arun K$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aALT '96 906 $aBOOK 912 $a996465872103316 996 $aAlgorithmic Learning Theory$9771965 997 $aUNISA LEADER 02137oam 2200517 450 001 9910827641303321 005 20190911103508.0 010 $a9781416625513 010 $a1-4166-2552-6 010 $a1-4166-2551-8 035 $a(OCoLC)1019839362 035 $a(MiFhGG)GVRL83S6 035 $a(EXLCZ)994340000000263899 100 $a20171221h20182018 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAttack of the teenage brain! $eunderstanding and supporting the weird and wonderful adolescent learner /$fJohn Medina 210 1$aAlexandria, Virginia :$cASCD,$d[2018] 210 4$d?2018 215 $a1 online resource (223 pages) $cillustrations 225 0 $aGale eBooks 311 $a1-4166-2549-6 320 $aIncludes bibliographical references and index. 327 $apt. 1. A bridge over an educational chasm -- pt. 2. Why rational teens make rash choices -- pt. 3. A better school for the teen brain : what adults can do -- pt. 4. Better school for the teen brain : what teens can do (and how adults can help). 330 $aThis book explores the neurological and evolutionary factors that drive teenage behavior and can affect both achievement and engagement. The author proposes a redesign of educational practices and learning environments to deliberately develop teens' cognitive capacity to manage their emotions, prioritize, and focus. 606 $aLearning, Psychology of 606 $aAdolescent psychology 606 $aExecutive functions (Neuropsychology) 606 $aCognition in adolescence 606 $aTeenagers$xEducation 615 0$aLearning, Psychology of. 615 0$aAdolescent psychology. 615 0$aExecutive functions (Neuropsychology) 615 0$aCognition in adolescence. 615 0$aTeenagers$xEducation. 676 $a370.15/23 700 $aMedina$b John$f1956-$0754355 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910827641303321 996 $aAttack of the teenage brain$93981208 997 $aUNINA