LEADER 05343nam 22006495 450 001 996466054403316 005 20200706005738.0 010 $a3-540-48096-X 024 7 $a10.1007/3-540-57370-4 035 $a(CKB)1000000000234045 035 $a(SSID)ssj0000321182 035 $a(PQKBManifestationID)11260282 035 $a(PQKBTitleCode)TC0000321182 035 $a(PQKBWorkID)10276823 035 $a(PQKB)11374565 035 $a(DE-He213)978-3-540-48096-9 035 $a(PPN)155229028 035 $a(EXLCZ)991000000000234045 100 $a20121227d1993 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAlgorithmic Learning Theory$b[electronic resource] $e4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings /$fedited by Klaus P. Jantke, Shigenobu Kobayashi, Etsuji Tomita, Takashi Yokomori 205 $a1st ed. 1993. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1993. 215 $a1 online resource (XI, 428 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v744 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-57370-4 327 $aIdentifying 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. 330 $aThis volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. In addition to 3 invited papers, 29 papers were selected from 47 submitted extended abstracts. The workshop was the fourth in a series of ALT workshops, whose focus is on theories of machine learning and the application of such theories to real-world learning problems. The ALT workshops have been held annually since 1990, sponsored by the Japanese Society for Artificial Intelligence. The volume is organized into parts on inductive logic and inference, inductive inference, approximate learning, query learning, explanation-based learning, and new learning paradigms. 410 0$aLecture Notes in Artificial Intelligence ;$v744 606 $aArtificial intelligence 606 $aMathematics 606 $aComputers 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/M00009 606 $aTheory of Computation$3https://scigraph.springernature.com/ontologies/product-market-codes/I16005 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 615 0$aArtificial intelligence. 615 0$aMathematics. 615 0$aComputers. 615 14$aArtificial Intelligence. 615 24$aMathematics, general. 615 24$aTheory of Computation. 615 24$aComputation by Abstract Devices. 676 $a006.3 702 $aJantke$b Klaus P$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKobayashi$b Shigenobu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTomita$b Etsuji$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYokomori$b Takashi$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996466054403316 996 $aAlgorithmic Learning Theory$9771965 997 $aUNISA